{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7ed50c0c",
   "metadata": {},
   "source": [
    "# 第一章 鸢尾花分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df9ec70f",
   "metadata": {},
   "source": [
    "## 导入包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b13e22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import IPython as ip\n",
    "import sklearn as sl\n",
    "import mglearn\n",
    "import random"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "575f463e",
   "metadata": {},
   "source": [
    "## 初识数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d699bcfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris_dataset = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "befd4cb3",
   "metadata": {
    "hide_input": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keys of iris_dataset: \n",
      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
     ]
    }
   ],
   "source": [
    "print(\"keys of iris_dataset: \\n{}\".format(iris_dataset.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6035776b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 150 (50 in each of three classes)\n",
      ":Number of Attributes: 4 numeric, predictive \n",
      "...\n"
     ]
    }
   ],
   "source": [
    "# DESCR：数据集的简要说明\n",
    "print(iris_dataset['DESCR'][ : 193] + \"\\n...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "480267d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target names: ['setosa' 'versicolor' 'virginica']\n"
     ]
    }
   ],
   "source": [
    "# tatget_names: 预测的花的品种\n",
    "print(\"Target names: {}\".format(iris_dataset['target_names']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f81769ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names: \n",
      "['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n"
     ]
    }
   ],
   "source": [
    "# feature_names：特征说明\n",
    "print(\"Feature names: \\n{}\".format(iris_dataset['feature_names']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "55555678",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# data 里面是花瓣花萼的长宽的测量数据，是一个 NumPy 数组\n",
    "print(\"Type of data: {}\".format(type(iris_dataset['data'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5570d668",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data: (150, 4)\n"
     ]
    }
   ],
   "source": [
    "# 150 朵花， 4 个测量数据（花瓣花萼的长宽）\n",
    "print(\"Shape of data: {}\".format(iris_dataset['data'].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c52d3634",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five rows of data: \n",
      "[[5.1 3.5 1.4 0.2]\n",
      " [4.9 3.  1.4 0.2]\n",
      " [4.7 3.2 1.3 0.2]\n",
      " [4.6 3.1 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]]\n"
     ]
    }
   ],
   "source": [
    "# 前 5 朵花的特征数值\n",
    "print(\"First five rows of data: \\n{}\".format(iris_dataset['data'][ : 5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fe6bdeff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Typy of target: <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# target 每朵花的品种，是一个 NumPy 数组\n",
    "print(\"Typy of target: {}\".format(type(iris_dataset['target'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8ead8d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of target: (150,)\n"
     ]
    }
   ],
   "source": [
    "# target 是一个一维数组\n",
    "print(\"Shape of target: {}\".format(iris_dataset['target'].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1e29d84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target: \n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "# 品种转换为 0-2 的整数\n",
    "print(\"Target: \\n{}\".format(iris_dataset['target']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5316049",
   "metadata": {},
   "source": [
    "## 训练数据和测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3c8b84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# iris_dataset['data']: 所要划分的样本特征集（数据集）\n",
    "# iris_dataset['target']: 所要划分的样本结果（标签）\n",
    "# random_state：随机数种子,(填0或不填，每次都会不一样)\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], iris_dataset['target'], random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c3891d38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (112, 4)\n",
      "y_train shape: (112,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape: {}\".format(X_train.shape))\n",
    "print(\"y_train shape: {}\".format(y_train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "82242a23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test shape: (38, 4)\n",
      "y_test shape: (38,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_test shape: {}\".format(X_test.shape))\n",
    "print(\"y_test shape: {}\".format(y_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "22649c72",
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris_dataframe = pd.DataFrame(X_train,\n",
    "                            columns = iris_dataset.feature_names)\n",
    "\n",
    "grr = pd.plotting.scatter_matrix(iris_dataframe,\n",
    "                        c = y_train, figsize = (15, 15), \n",
    "                        marker = 'o', hist_kwds = {'bins' : 20}, \n",
    "                        s = 60, alpha = .8, cmap = mglearn.cm3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d3d3543",
   "metadata": {},
   "source": [
    "## 构建第一个模型：K 近邻算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4492b051",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "82e13224",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3d756f95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_new.shape: (1, 4)\n"
     ]
    }
   ],
   "source": [
    "X_new = np.array([[4, 3.1, 1, 0.2]])\n",
    "print(\"X_new.shape: {}\".format(X_new.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1fda6cb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: [0]\n",
      "Predicted target name: ['setosa']\n"
     ]
    }
   ],
   "source": [
    "prediction = knn.predict(X_new)\n",
    "print(\"Prediction: {}\".format(prediction))\n",
    "\n",
    "print(\"Predicted target name: {}\".format(\n",
    "    iris_dataset['target_names'][prediction]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0a9f1f8",
   "metadata": {},
   "source": [
    "## 评估模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "43aeb7f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set predictions: \n",
      "[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0\n",
      " 2]\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "print(\"Test set predictions: \\n{}\".format(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ecef6d55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "# 使用 np 对象的 mean 方法来计算精度\n",
    "print(\"Test set score: {:.2f}\".format(np.mean(y_pred == y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8787eef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "# 使用 knn 对象的 score 方法来计算精度\n",
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26d73ca",
   "metadata": {},
   "source": [
    "## 总结"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cdea6992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.97\n"
     ]
    }
   ],
   "source": [
    "# 第一章必要代码\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris_dataset['data'], iris_dataset['target'], random_state=0)\n",
    "\n",
    "# 创建K近邻分类器实例\n",
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "# 训练模型\n",
    "knn.fit(X_train, y_train)\n",
    "\n",
    "# 输出测试集的评分\n",
    "print(\"Test set score: {:.2f}\".format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b0765bc",
   "metadata": {},
   "source": [
    "# 第二章 监督学习"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e443c331",
   "metadata": {},
   "source": [
    "## 监督学习算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd2df422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X.shape: (26, 2)\n"
     ]
    }
   ],
   "source": [
    "# 生成数据集\n",
    "X, y = mglearn.datasets.make_forge()\n",
    "\n",
    "# 数据集绘图\n",
    "mglearn.discrete_scatter(X[:, 0], X[:, 1], y)\n",
    "\n",
    "plt.legend([\"Class 0\", \"Class 1\"], loc=4)\n",
    "\n",
    "plt.xlabel(\"First feature\")\n",
    "plt.ylabel(\"Second feature\")\n",
    "\n",
    "# 显示图形\n",
    "plt.show()\n",
    "\n",
    "print(\"X.shape: {}\".format(X.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a347e78f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Target')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = mglearn.datasets.make_wave(n_samples=49)\n",
    "\n",
    "plt.plot(X, y, 'o')\n",
    "\n",
    "# 设置 y 轴的范围为 -3 到 3\n",
    "plt.ylim(-3, 3)\n",
    "\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel(\"Target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc327259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cancer.key(): \n",
      " dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])\n"
     ]
    }
   ],
   "source": [
    "# 通过 scikit-learn 模块的 load_breast_cancer 函数来加载数据\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "print(\"cancer.key(): \\n {}\".format(cancer.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33bac4eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of cancer data: (569, 30)\n"
     ]
    }
   ],
   "source": [
    "# 打印数据集（569 个数据，30 个特征）\n",
    "print(\"Shape of cancer data: {}\".format(cancer.data.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50fd0b6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample count per class: \n",
      " {np.str_('malignant'): np.int64(212), np.str_('benign'): np.int64(357)}\n"
     ]
    }
   ],
   "source": [
    "print(\"Sample count per class: \\n {}\".format(\n",
    "    {n : v for n, v in zip\n",
    "     (cancer.target_names, np.bincount(cancer.target))\n",
    "    }\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8aa039b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature names:\n",
      "['mean radius' 'mean texture' 'mean perimeter' 'mean area'\n",
      " 'mean smoothness' 'mean compactness' 'mean concavity'\n",
      " 'mean concave points' 'mean symmetry' 'mean fractal dimension'\n",
      " 'radius error' 'texture error' 'perimeter error' 'area error'\n",
      " 'smoothness error' 'compactness error' 'concavity error'\n",
      " 'concave points error' 'symmetry error' 'fractal dimension error'\n",
      " 'worst radius' 'worst texture' 'worst perimeter' 'worst area'\n",
      " 'worst smoothness' 'worst compactness' 'worst concavity'\n",
      " 'worst concave points' 'worst symmetry' 'worst fractal dimension']\n"
     ]
    }
   ],
   "source": [
    "# 打印特征名称\n",
    "print(\"Feature names:\\n{}\".format(cancer.feature_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6b0485",
   "metadata": {},
   "source": [
    "### K近邻算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1daa88c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GBbCc4GlhIiIi0Tm0g6hGo4Ferzd9Pnz4cHzwwQeYOnUqfH19bRas2Pr27Ytr166hvLwcoaGhYodDREQkKof22QgODkZERITpQyqVIjQ01KJEw5VkZWUBANLS0kSOhIiIyHWwXbkVEhMTAQAff/yxyJEQERG5DrssMa/T6eyxW9FJJBJIpVIYDAZUV1e7XT8RIiIie2Blw0p/+9vfAAC//e1vRY6EiIjINXAhNisZDAZ4eHgA4FMpRETUs3GJeTuRSn/6L2toaBAxEiIiItfAZKMb1q9fDwB47733RI6EiIjI+fE2SjfcuHEDvXv3BsBbKURE1HPxNood9erVy/Tvf//73xg7bjzUarWIERERETkvJhvdtGLFCgDAK6+8gmPqI1i/3v1WuyUiIrIF3kbppoaGBsjlcgASeCoGQFJ7DT/+eAXBwcFih0ZEROQQvI1iZ8ZF5yCVos/MN9AqCMjOzhY3KCIiIidklw6i7ujKlSuoqKho97WAwCDc6HcPeinDIIsei8yNmzBp0qR22/Tt2xcDBgxwYKRERETOhcmGheY88yy+PnSw3dckUg/0mTIVAOA7agpKPl6G0aNHt9tmwn/9DIcPfOWwOImIiJwNkw0LPTdvLnKPH8cNQYLAh1PQSxkGqbcvegW2LTXvPfAu9Hv+Axga9bihLYNu/yb0kgiYPzdZ5MiJiIjExTkbFkpKSkLeyVwMiQiH7otM3NBcNCUaRr0CQ3Hj+kXovshEdOQg5J3MRVJSkkgRExEROQcmG1aIi4tD3slczJrx39DuSUeLvrLd6y36Smj3pmP2zBnIO5mLuLg4kSIlIiJyHkw2rOTj44OJEydA4uEBD29fAIChqR4A4OHtC4mHByZOnPB/j8USERERk41uyMvLg6xPBASDAdq976Ps/ceh3fs+BMEA75BByMvLEztEIiIip8FkoxuOnziJVg9vXN+ahpYLaixZsgQtF9S4/rdXYfD0xrHjuWKHSERE5DSYbFipsbER5859j6bL3yNC6YO8k7lYs2YN8k7mYpBChqbLZ3Hu3PdobGwUO1QiIiKnwEdfrdTQ0IB7ho/AyHvvwfr1601zM4yTRxctWoTT37UlG97e3iJHS0REJD6ujdINBoMBUunti0LmXiciInIHXBvFjswlEkw0iIiIfsKrIhEREdkVkw0iIiKyKyYbREREZFdMNoiIiMiumGwQERGRXTHZICIiIrtiskFERER2xWSDiIiI7IrJBhEREdkVkw0iuiNqtRoTxj0ItVotdihE5KSYbBDRHVm/bh2+Vh9H5vr1YodCRE6KyQYRdZtGo8H27TkYqpQiJ2cbNBqN2CERkRNiskFE3ZaVlQUIBvzzSRkgGJCdnS12SETkhDzFDoCIXMOVK1dQUVHR7msfbNqAx2I9EBvsgZmxHvjzxkxMmjSp3TZ9+/bFgAEDHBgpETkbJhtEZJHkZ+bgq0Nft/uah1SCzckyAMDC+3th4kclGD16dLttHv6vCfjywGGHxUlEzofJBhFZJGneczh6PBe9hGas/2VvxIV4IMhbgsigtrux48M9cWGxD6oaBZy93opFnzfjhqQ3np07X+TIiUhsEkEQBLGDqKmpQUBAAKqrq+Hv7y92OER0G2fPnsXsx2ag9IdibHykF5Lu7d1hm6xvmvHS5zcQGTUEn+X8A3FxcSJE6l4EQUBLSwtaW1vFDoV6GA8PD3h6ekIikXT6uqXXb1Y2iMhicXFxOHHyFJ6bPx/Jf/87fjHYE/38fppnXl5rwNydjZjz5JP4y//+L+RyuYjRuofm5maUl5ejvr5e7FCoh5LL5ejXrx969+74x4WlmGwQkVV8fHwwYeJE5OR8hiBZ2187tU0C/LwkCJJJ4CEB6hsamGjYgMFgQElJCTw8PNC/f3/07t37tn9hEtmaIAhobm7G9evXUVJSgujoaEil3XuIlckGEVktLy8Pd/ftjVYDMG9nIz76phnzRvTG+ke8MDRYip07dyI9PR1LliwRO1SX1tzcDIPBgLCwsDtK3gRBgFarhV6vh6+vL5RKJZMWsohMJkOvXr1w8eJFNDc3w9vbu1v7YZ8NIrJa3olj8PNswZjNTfjsvBRLlizB389LMeZ/m6CQSeApBV555RVs2LBB7FDdQnf/mtTpdMjIyED00FiEhIQgMjISISEhiB4ai4yMDOh0OtsGSm6pu99/7fZhgzhMzp07h7Fjx8LX1xcPPfQQioqKbLl7InICjY2NOHOuAF9faoVEEYnck3lYs2YNck/mQQiKwH8utQISDwDAwoULsXnzZpEj7plUKhXCwsOxJC0NFZ6hCJ7+Ovo8sRLB019HhWcolqSlISw8HCqVSuxQqQewabIxZ84cTJs2DYWFhYiNjUVKSootd09ETqChoQGjht+NX82bhxMnT5meNjFOHp03dy5Gj7gHp0+fBgDMnz8fW7duFTPkHkelUmHK1Kkw9I3FgJSPoJz2Gnxix0MWMQI+seOhnPYaBqR8BEPfWEyZOtVlEo7S0tJu3f7p7jh7OHToECIiIsQOA3/9618RFhYGPz8/LF682O5POtks2aiqqoKfnx/S0tLQv39/zJgxA2fPnrXV7onISQQFBeFo7in87+bNHeYR+Pj4YPOWLVCfyMPw4cNx4cIFAMAzzzyDnJwcMcLtcXQ6HWbNng2viJEInvEmPHyDOt3OwzcIwTPehFfESMyaPdvmt1QmTZqEjz76yKb7DA8PR1VVlcPGuavCwkI8//zzWL9+PXJzc7F37167VyBtlmwEBQXh66+/Ru/evdHc3IzPPvsMI0eOtNXuiciJmLuHa3w9KioKBQUFAIDZs2fjX//6l91j6+mysrJQV18PRWIqJFKPLreVSD0QlLgY9fX1LrGujVQqRWBgoMPGuautW7fiZz/7GaZPn47Y2FgsXLgQf/3rX+36nnaZICqXy/H5559j/W2WnG5qakJNTU27DyJyT0OHDsV3330HAJg2bZrLlOxdkSAIWJe5AfKYcbetaNzK01cBWUw81q7PhC16PKakpEAikeDw4cOYN28eJBJJu1vqK1aswNy5c7Fv3z6MGDECy5YtM7125MgRjBw5EnK5HGPGjOlQHe/sdojxtsSuXbswaNAgKBSKDtee7oy7dOkS4uPjERAQgJSUFNx33334n//5H7PHn5+fb5q7OG7cOHz//ffm/9MAfPjhhwgPD4efnx9mzpwJvV5veu2TTz5BZGQkfHx8kJCQgGvXrln02u0cOXIE8fHxps8feOABHD161Cbn/3bskmycOHECo0aNQmpqaqevr1q1CgEBAaaPsLAwe4RBRE7i7rvvxqlTpwAAkydPxsGDB0WOyD1ptVoUFxVCFhNvfuObyKLjUVxUiMrKyjuOIT09HVVVVRg3bhwyMzNRVVWF9PT0dtucOXMGS5cuxfLly7FgwQIAbT1FZs2ahZkzZ+KHH37A+PHj8eqrr1r0nlqtFqtXr8bevXvxu9/9DmlpaWhsbLyjca+//jpGjhyJvLw87Nu3D6+99hpee+21LvdXXV2NyZMnY9q0aTh//jzuv/9+PP3002bj+P777/Hiiy/iL3/5C86ePQuNRmN6kkuv1yM5ORmrV6/G2bNn4e3tjdWrV5t9rStXr15FcHCw6XOlUomWlhZotVqzY7vLZn02rl+/jsuXL2PkyJEYNWoUfv/732PUqFGorq5GQEBAu22XLVuGV155xfR5TU0NEw4iNzdy5EgcO3YMDz74IH72s5/hP//5D8aNGyd2WG7F+New1NvXqnHG7Wtra6FUKu8oBplMBplMBk9PT8jl8k5vX5w5cwYFBQUdJkrm5uYiJCQEZ86cQU1NDc6fP2/Re+r1emzcuBF33XUXoqOjsXjxYlRUVGDQoEHdHnf69Gmkp6djyJAhGDt2LIqLi/H44493ub89e/ZAoVCYqjVvvfUWxo4dazb+wYMH4/Lly/D398exY8fQ0tJiOnZju/CGhgaEhoZi165dpnFdvWbOzVUM47/tOYnWZpWN/Px8TJkyxfS5MejO7u16eXnB39+/3QcRub8HHngAX3/dtnLs+PHjceLECZEjci++vm1Jg6FRb2bL9ozb+/n52Tymzjz66KMdEg2pVIoNGzagf//+eO6556DRaCx+QiIoKAjDhw8HAFNLbUtuCXQ1LiYmBkePHkVjYyNOnz6NYcOGmd1fWVlZu+MKCgrCE088YXZcU1MTFi5ciAEDBuDtt9+GRCIxHbtMJsOnn36KP//5zwgODsYvf/lL08Trrl7rSr9+/XD9+nXT51qtFr169YJCoTA7trtslmyMGTMGjY2NyMzMxOXLl7FmzRo89NBDDvvmJSLXMH78eHz11VcA2pKPb775RtyA3IhSqURUdAwaC9VWjWsoUiMqOsamFxupVHrbC76Pj0+Hrx0+fBibNm3Cd999h/z8fDz//PMWv1d3/2DtatyQIUOQnp4OPz8/DBs2DNOmTTO7v7CwMJSWlpo+1+v1uPvuu3H16tUux2VkZECr1aK8vByHDx9uV/GrrKxE3759oVarUVFRgT59+pg683b1WlceeughqNU/fY8cO3YM48aNc43KRmBgIP7xj3/ggw8+wLBhw1BRUWH32a1E5Jp+9rOf4fPPPwfQdnvF0kl01DWJRILFC19CXeERtOote9SzRV+JhkI1UhcttOnFZsiQIThw4ACuXr2KgwcPmq1S1NTUQCKRQK/X48iRI3jllVfsOmGxKw0NDVi/fj2++uornD9/Hjk5ORZ10ZwyZQq0Wi1WrVqFy5cv45133kFrayv69u3b5TjjQxKVlZX4+OOPsXHjRtOxX7t2DZMmTcK+fftMjycbDAazr3XlqaeewqFDh7Bz504UFBRgw4YNeOaZZ8yOuxM2nSA6adIknD59GrW1tVCpVGbvlxFRzzV58mT885//BNA2gbSwsFDcgNxEcnIyfORyVKrWQjB0fYEXDK3QqdZBLpcjKSnJpnEsX74cJSUlCA8Px/z5881eBCdPnoxf/OIXGDVqFFJSUrBgwQL8+OOPqKiosGlclpDJZEhMTERiYiKGDh2K3r17Y+bMmWhqaupyXEBAAD7//HPs3LkTw4YNw9GjR7Fjxw6zSVxqaioaGhoQExODLVu2YP78+aaKX2xsLP70pz/hxRdfRFRUFM6fP493333X7GtdGTJkCD788EMsXLgQ999/P6ZMmYJ58+ZZ9p/TTRJBrNTxJjU1NQgICEB1dTXnbxD1MJ999pnpvnZxcTEGDx4sckTOo7GxESUlJYiMjLRqASxjB1GviJEISlwMT9+Ot0da9JWoUq1DU2k+9u7Zg4SEBFuG7tIOHDiAX//619i5cycCAgLwww8/YMqUKdi/fz/uuecescNzuK6+Dy29fnPVVyIS1eOPP46mpiYkJSUhKioKFy9eRHh4uNhhubTExETs2b0bs2bPRvmmeZDFxEMWHQ+pty8MjXo0FKnRUKiGXC5notGJe++9F1FRURg9ejT0ej369euHlJQU3H333WKH5rKYbBCR6J599lk0NTVhwYIFGDRoEK5cuYL+/fuLHZZLS0xMRNmlS8jOzsba9Zko3vVT/4Wo6BikrlmD5OTkDq0JqG2i7Y4dO8QOw60w2SAip/Dcc8+hqakJixYtwoABA0yz66n7AgMDkZqaisWLF6OyshK1tbXw8/ODQqFwmoXJqGdgskFETmPhwoVobGzEq6++ir59+0Kj0dxxkylqe0pFqVTy/5JEY5d25URE3ZWWloaVK1cCAIKDg22+GikROR6TDSJyOm+88QbefPNNAG1dGGtra0WOiIjuBJMNInJK/+///T+kpaUBaOv0WFdXJ3JErm3//v0YcU8c9u/fL3Yo1AMx2SAip/XHP/4RL730EoC2dT8aGhpEjsg1CYKA5W/8BqfPnMPyN34jWmdO6rmYbBCRU8vMzMTcuXMBAHK53GwXR+roiy++wLETuVjyYG8cO5GLL774QuyQuqW0tLRbT9F0d5w9HDp0qMMidGLJz89HbGysQ96LyQYROb0tW7aYuox6e3vjxo0bIkfkOgRBwIrfLseDYb3xpwQvPBjWGyt+u9zu1Y1Jkybho48+suk+w8PDUVVl2ZovthjnzoYOHYpRo0aZXSTOVphsEJFL+PTTT/Hoo48CaFsOvKWlReSIXIOxqrFigickEglWTPB02eqGVCpFYGCgw8a5s6+++gqffPKJw96PyQYRuYxdu3bh5z//OQCgV69eZlcS7elurmokRHkAABKiPOxa3UhJSYFEIsHhw4cxb948SCQSpKSkmF5fsWIF5s6di3379mHEiBFYtmyZ6bUjR45g5MiRkMvlGDNmDM6ePdtu353dDjHelti1axcGDRoEhUKB9evX3/G4S5cuIT4+HgEBAUhJScF9992H//mf/zF7/Pn5+Rg7dix8fX0xbtw4i1c0/vDDDxEeHg4/Pz/MnDkTer3e9Nonn3yCyMhI+Pj4ICEhAdeuXbPota4MHDgQoaGhFm1rC0w2yCqCIECj0aC0tBQajYYTzcjh9u/fjwcffBAA4OnpadGS2j3VrVUNAHavbqSnp6Oqqgrjxo1DZmYmqqqqkJ6e3m6bM2fOYOnSpVi+fDkWLFgAoG1p9FmzZmHmzJn44YcfMH78eLz66qsWvadWq8Xq1auxd+9e/O53v0NaWhoaGxvvaNzrr7+OkSNHIi8vD/v27cNrr72G1157rcv9VVdXY/LkyZg2bRrOnz+P+++/H08//bTZOL7//nu8+OKL+Mtf/oKzZ89Co9Fgw4YNAAC9Xo/k5GSsXr0aZ8+ehbe3N1avXm32NWfDDqJkEZ1Oh6ysLKzL3IDiop+WAo+KjsHihS8hOTmZZUpymKNHj2L48OH47rvv4OHhgdbWVkil/NvpZp1VNYxurm4kJCTYdPKkTCaDTCaDp6cn5HJ5p78Xzpw5g4KCgg4TJXNzcxESEoIzZ86gpqYG58+ft+g99Xo9Nm7ciLvuugvR0dFYvHgxKioqMGjQoG6PO336NNLT0zFkyBCMHTsWxcXFePzxx7vc3549e6BQKEzVmrfeegtjx441G//gwYNx+fJl+Pv749ixY2hpaTEdu4eHBzw9PdHQ0IDQ0FDs2rXLNK6r15wNfzrJLJVKhbDwcCxJS0OFZyiCp7+OPk+sRPD011HhGYolaWkICw+HSqUSO1TqQb799lvTcvQeHh6sst2is6qGkdhzNx599NEOiYZUKsWGDRvQv39/PPfcc9BoNBbfJgsKCsLw4cMBtM3nAWDR90NX42JiYnD06FE0Njbi9OnTGDZsmNn9lZWVtTuuoKAg08TmrjQ1NWHhwoUYMGAA3n77bUgkEtOxy2QyfPrpp/jzn/+M4OBg/PKXv8SFCxfMvuZsmGxQl1QqFaZMnQpD31gMSPkIymmvwSd2PGQRI+ATOx7Kaa9hQMpHMPSNxZSpU5lwkEMVFxebFmvz9PTscIHpqY2suqpqGNl77oZUKr3tfn18fDp87fDhw9i0aRO+++475Ofn4/nnn7f4vfz9/bsVY1fjhgwZgvT0dPj5+WHYsGGYNm2a2f2FhYWhtLTU9Ller8fdd99t9omPjIwMaLValJeX4/Dhwxg3bpzptcrKSvTt2xdqtdq0OOGSJUvMvuZsmGzQbel0OsyaPRteESMRPONNePgGdbqdh28Qgme8Ca+IkZg1ezbXsiCHunr1Knx9fWEwGBAQEGC6wPXkRlZdVTWM7F3dGDJkCA4cOICrV6/i4MGDZqsUNTU1kEgk0Ov1OHLkCF555RXRzllDQwPWr1+Pr776CufPn0dOTo5Ft+mmTJkCrVaLVatW4fLly3jnnXfQ2tqKvn37djmupqYGQFvy8PHHH2Pjxo2mY7927RomTZqEffv2mX63GucpdfWas2GyQbeVlZWFuvp6KBJTIZF2/teRkUTqgaDExaivr0d2draDIiRqu2gaL1S1tbUYMGAAAPdpZGUtS6oaRvasbixfvhwlJSUIDw/H/PnzzV4EJ0+ejF/84hcYNWoUUlJSsGDBAvz444+oqKiwaVyWkMlkSExMRGJiIoYOHYrevXtj5syZZhvKBQQE4PPPP8fOnTsxbNgwHD16FDt27DA7JyY1NRUNDQ2IiYnBli1bMH/+fHzzzTcAgNjYWPzpT3/Ciy++iKioKJw/fx7vvvuu2decjURwgnS/pqYGAQEBqK6u7nY5jGxLEARED43FVc9QBE/regb2zTS7ViO0pQJF5wucpmMf9QyCIJj++oyKikKIUgGUn4Z6nhfitzQB/e6F+thxl/q+bGxsRElJCSIjI+Ht7W3RGJVKhcmTJ2Pf03IkDjH/DIDqQgsmb63Hvn37kJiYeKchu4UDBw7g17/+NXbu3ImAgAD88MMPmDJlCvbv34977rlH7PAcrqvvQ0uv33wahTql1WpRXFSI4OkzrRoni45H8a7VqKyshFKptFN0RB0ZJ9V5eHiguLgYxcXF2Pe03HS7YPLWtuqGO19QjVWNKIUnguUSnCo3P8EyWC5BlMLTLk+muKp7770XUVFRGD16NPR6Pfr164eUlBTcfffdYofmsphsUKeMDWWk3r5WjTNuX1tby2SDHE4qlaKlpQVevTwxqp+000ZW7nxBbW5uxuWyS7hc2YL7PrSuw2rz5TI0NzfDy8vLTtG5DqVSiR07dogdhlthskGd8vVtSxoMjXozW7Zn3N7Pz8/mMRFZYv/+/WgVgP/3X94dGlm5e3XDy8sL6uO5uH79utVj+/Tpw0SD7IbJBnVKqVQiKjoGFYVq+MSOt3hcQ5EaUdExUCgUdoyOqHNiNbJyJmFhYQgLCxM7DKJ2+DQKdUoikWDxwpdQV3gErXrLVkts0VeioVCN1EUL3fYXOTk3Z25kRdSTMdmg20pOToaPXI5K1VoIhq4nmgmGVuhU6yCXy5GUlOSgCIl+4gyNrIioc0w26LYCAwORs20bmkrzodmxEi36yk63a9FXQrNjJRpL87E9J4drpJAonKGRlTNTq9UYO2481Gq12KFQD8Q+G2SWSqXCrNmzUV9fD1lMPGTR8ZB6+8LQqEdDkRoNhWrI5XJsz8lBQkKC2OFSDyQIAuIffMDUV6Or23iCILhM343u9Nm4nTlznsKnn36COXOewscfb7VRhNQT2KLPBisbZFZiYiLKLl1C+po1CG2pgGbXalz7bLmpgVf6mjW4XFbGRINEY0lVw6gnVjc0Gg22b98OT8VA5OTkQKPRiB2S2ygtLbU4YVWpVIiJiYGPjw+efPJJ1NfX2zk658FkgywSGBiI1NRUFJ0vgEajQUlJCTQaDYrOFyA1NRUBAQFih0g9VGeNrMx93NzIygmKu3aXlZWFVkFAn5lvoFUQHLKkwKRJk/DRRx85/T4dRafT4fHHH8crr7yC77//HqWlpXjnnXfEDsth+OgrWUUikUCpVLJhFzkNNrJq78qVKx3WE9mw6c+QxcSjlzIMsuixyNy4CZMmTWq3Td++fU3rypDt7dixAwMHDkRKSgoA4LXXXsOSJUt6TMLBygYRuTRjI6u8vDyrP9THc90q0QCAOc88i9GjR7f7KPnhB/iOnAIA8B01BSXFxR22eepZ2zxFlpKSAolEgsOHD2PevHmQSCSmCywAXLhwAQ8//DD8/f0xevRo5Obmml7Lz8/H/fffD7lcjuHDh+Po0aMW7fN2Dh06ZFoILigoCGPHjkVBQYHp9RUrVmDu3LnYt28fRowYgWXLllkU5+7duzFkyBAoFAqLKy1HjhxBfHy86fMHHngAly5dQllZmUXjXR0rG0Tk8tjI6ifPzZuL3OPHcUOQIPDhFPRShkHq7YtegaEAAO+Bd6Hf8x/A0KjHDW0ZdPs3oZdEwPy5yTZ5//T0dPzhD3/A1KlT8dRTT+Gpp54yJXQtLS2YNm0ali5dis2bN+Mf//gH5syZg6KiIkgkErz00ksYP348du7ciQ8//BCLFi1CXl5el/s0p6ysDHK5HPn5+Vi9ejXmzJmDU6dOmeZZnDlzBkuXLsVvf/tbjBw50myc165dwxNPPIGMjAxMmjQJTz/9tEVxXL16td0ibsbqcEVFRY/43mWyQUTkRpKSknDfffdh5mOzcOGLTAT+4kX43v3zdtv0CgyF/ruvoNu/EdFRg7E9Zxvi4uJs8v4ymQwymQyenp6Qy+XtHoU/fvw4CgoK8PLLL5u+Vl1djfLycvTv3x9eXl5oamqCTCbDW2+9hbfeesvsPs3p3bs3Vq9eDblcjt///vdQKBS4ePEiIiIiALQlGwUFBabPzcX55ZdfYvDgwXjuuecAtFVHHnnkEYtiuXl+kPHfzvw0lC3xNgoRkZuJi4tD3slczJrx39DuSe/QI6dFXwnt3nTMnjkDeSdzbZZomHP58mUMGjQI33zzjemjpKQEISEhAIANGzagrKwMkZGRGDVqFPbt23fH76lQKCCXywEAQUFBkMlk7ea0PProo+0SDXNxlpeXt6tEDB482KI4+vXr127NGq1WCwAIDQ3t7qG5FCYbRERuyMfHBxMnToDEwwMe/7cas6Gp7VFLD29fSDw8MHHiBNOF2NakUmmHJ30GDhyIa9euoU+fPoiIiMDAgQPx7rvvQqvVwmAw4Nq1a8jJyYFWq8WCBQvw5JNPwmAwdLlPczQajWkVa41Gg4aGBvTr18/0uo+PT4cxXcXZp08f/Pjjj6ZtL126ZFEcDz30ULuGaseOHUNERESPmZTLZIOIyE3l5eVB1icCgsEA7d73Ufb+49DufR+CYIB3yCDk5eXZ7b2HDBmCAwcO4OrVqzh48CBaW1sxZswYDBgwAK+++irKysqwatUq7NmzByEhIZBKpZgzZw4yMjJQXl4OAO0Sjdvt05yWlhYsXboUFy9exBtvvIH77rsP4eHhXY7pKs6EhAQUFBQgKysLxcXFWLFihUX/H9OnT0d5eTk2btyI0tJSvPfee3jmmWcsGusOmGwQEbmp4ydOotXDG9e3pqHlghpLlixBywU1rv/tVRg8vXHseK75nXTT8uXLUVJSYnoaxGAwoFevXvjXv/6FgoICxMXFYffu3di5cyc8PNrWsvnkk0/w6aefIjo6Gn/84x+xZcsWSKXSLvdpTnh4OARBQFxcHL799lts3Wq+e2pXcQ4cOBBbt27F22+/jfHjx2PcuHEW/X8EBATgs88+w5o1axAXF4fIyMh2T7+4O7YrJyJyUnfSrryxsRG+fn5obWlB7LA40yTQs2fPYuZjs3C+4Bw8PD2hr62941bozurQoUOYO3cuSktLxQ7FpdmiXTmfRiEickMNDQ24Z/gIjLz3Hqxfv940N8M4eXTRokU4/d33aGxsdNtkg5wHkw0iIjcUFBSEvNzj7W5DGPn4+GDLli0wGAydvu4uJk2axKqGk3Df7zIioh7OXCLhzokGORd+pxEREZFd2TTZ+OGHHzBx4kT4+flh0qRJuHjxoi13T+TWBEGARqNBaWkpNBpNj1iNlIh6BpsmG88//zzCw8Nx5swZKJVKLFy40Ja7J3JLOp0OGRkZiB4ai5CQEERGRiIkJATRQ2ORkZEBnU4ndohERHfEZslGc3MzDhw4gGXLlmHQoEGYP38+vv76a1vtnsgtqVQqhIWHY0laGio8QxE8/XX0eWIlgqe/jgrPUCxJS0NYeDhUKpXYoZKLU6vVmDDuwXZdLIkcxWZPo9y4cQPvvvsuIiMjAbT1fZfJZJ1u29TUhKamJtPnNTU1tgqDyGWoVCpMmToVXhEjMSAxFR6+Qe1e94kdj0B9FSpVazFl6lTs2b0biYmJIkVLrm79unX4Wn0cmevXt1vqnMgRbFbZ8PHxwauvvgqZTIYbN24gIyMDzz77bKfbrlq1CgEBAaaPnrC8LtHNdDodZs2eDa+IkQie8WaHRMPIwzcIwTPehFfESMyaPZu3VKhbNBoNtm/PwVClFDk526DRaMQOyW2UlpZatXLrhQsXEBcX1+N+lm3+NEpLSwvmzJkDDw8P/O53v+t0m2XLlqG6utr0UVZWZuswiJxaVlYW6urroUhMhUTq0eW2EqkHghIXo76+HtnZ2Q6KkNxJVlYWIBjwzydlgGBwyPfRpEmT8NFHHzn9Ph0pMTER0dHROHfunNihOJxNm3oZDAY88cQTuHjxIr788svb3kbx8vKCl5eXLd+ayGUIgoB1mRsgjxl324rGrTx9FZDFxGPt+kwsXrzYqr+kqGe5cuVKuyXUAeCDTRvwWKwHYoM9MDPWA3/emIlJkya126Zv3749ZgVSsWRlZeHChQt46KGHxA7F4Wxa2Vi+fDmKi4vx1VdfISjIsl+iRD2NVqtFcVEhZDHW3TeXRcejuKgQlZWVdoqM3EHyM3MwevTodh/FP5Rg4f29AAAL7++FC8UlHbaZ++xTNnn/lJQUSCQSHD58GPPmzYNEIkFKSorp9QsXLuDhhx+Gv78/Ro8ejdzcnxaDy8/Px/333w+5XI7hw4fj6NGjFu3zdg4dOmRatC0oKAhjx45FQUGB6fUVK1Zg7ty52LdvH0aMGNFuYbSu4ty9ezeGDBkChUJhVaUlNDQUAwcOtHh7d2KzZOPixYtYs2YNNm7cCEEQoNPpoNPpLFqVj6gn0ev1AACpt69V44zb19bW2jwmch9J856DXOaNAG8p/jrDG3nP+6BokQ/GhbcVsseHe+LCYh/kPe+Dv85o204u88azc+fb5P3T09NRVVWFcePGITMzE1VVVUhPTwfQdpt92rRpePbZZ3HmzBk8++yzmDNnjqmnzEsvvYTx48fjwoULeOyxx7Bo0SKz+zSnrKwMcrkc+fn5GDFiRLv3A4AzZ85g6dKlWL58ORYsWGA2zoqKCjzxxBN4/fXXceLECXz++ec2+X9zdza7jXLo0CE0NjZ2mOVcUlKCiIgIW70Nkcvz9W1LGgyNeqvGGbf38/OzeUzkPpKSknDfffdh9mMz8MKeYmx8BEi6t3e7bSKDpPj3N8146fMbiIyKxmc5/0BcXJxN3l8mk0Emk8HT0xNyuRyBgYGm144fP46CggK8/PLLpq9VV1ejvLwc/fv3h5eXF5qamiCTyfDWW2/hrbfeMrtPc3r37o3Vq1dDLpfj97//PRQKBS5evGi6Lp05cwYFBQXtrlNdxfnll19i8ODBeO655wC0VUceeeQR6/+jehibVTaSk5MhCEKHDyYaRO0plUpERcegsdC6fgcNRWpERcdAoVDYKTJyF3FxcThx8hSmzZiF5H82ory2fYW5vNaAuTsbMX3mbJw4ecpmiYY5ly9fxqBBg/DNN9+YPkpKShASEgIA2LBhA8rKyhAZGYlRo0Zh3759d/yeCoXCtOJtUFAQZDJZuzktjz76aIfrVFdxlpeXt3uCcvDgwXccY0/AtVGIHEwikWDxwpdQV3gErfoqi8a06CvRUKhG6qKFnBxKFvHx8cGEiRPh6SFBkKzte6a2qe32QZBMAk8PCSZMnGi6ENuaVCrt0HJ/4MCBuHbtGvr06YOIiAgMHDgQ7777LrRaLQwGA65du4acnBxotVosWLAATz75ZLtb8Z3t0xyNRmO6danRaNDQ0IB+/fqZXvfx8ekwpqs4+/Tpgx9//NG07aVLl6yKp6diskEkguTkZPjI5ahUrYVgaO1yW8HQCp1qHeRyOZKSkhwUIbmDvLw83N23N1oNwLydjfD/Qy1+tbMRBgG4q09v5OXl2e29hwwZggMHDuDq1as4ePAgWltbMWbMGAwYMACvvvoqysrKsGrVKuzZswchISGQSqWYM2cOMjIyUF5eDgAd5vx1tk9zWlpasHTpUly8eBFvvPEG7rvvPoSHh3c5pqs4ExISUFBQgKysLBQXF2PFihXd/j/qSZhsEIkgMDAQOdu2oak0H5odK9Gi7/wJkxZ9JTQ7VqKxNB/bc3KsuldNlHfiGPw8WzBmcxM+Oy/FkiVL8PfzUoz53yb49WrByRPH7Pbey5cvR0lJielpEIPBgF69euFf//oXCgoKEBcXh927d2Pnzp3w8GjrNfPJJ5/g008/RXR0NP74xz9iy5YtkEqlXe7TnPDwcAiCgLi4OHz77bfYunWr2TFdxTlw4EBs3boVb7/9NsaPH49x48Z1/z+pB5EITrC0ZE1NDQICAlBdXQ1/f3+xwyFyGJVKhVmzZ6O+vh6ymHjIouMh9faFoVGPhiI1GgrVkMvl2J6Tg4SEBLHDJQdrbGxESUkJIiMj4e3tbfVYPz9ftLS04q5hQ02TQM+ePYvZj83A2YJCeHp6oLZWb/W+XcWhQ4cwd+5clJaWih2KS+vq+9DS67dNm3oRkXUSExNRdukSsrOzsXZ9Jop3rTa9FhUdg9Q1a5CcnIyAgAARoyRX1NDQgFHD78bd947CuvXrTXMzjJNHFy9ahLPffYPGxka3TTbIebCyITJBEKDVaqHX6+Hr6wulUskJgD2UIAiorKxEbW0t/Pz8oFAo+L3Qw91JZQNom/Nw820Ia18nAmxT2eB3mUh0Oh0yMjIQPTQWISEhiIyMREhICKKHxiIjI6PHLdJDbU+pKJVKREREMOkkmzCXSDDRIEfhd5oIVCoVwsLDsSQtDRWeoQie/jr6PLESwdNfR4VnKJakpSEsPBwqlUrsUImIiO4Y52w4mEqlwpSpU+EVMRIDElM7LMTlEzsegfoqVKrWYsrUqdizezcSExNFipaInAGXfSAx2eL7j3M2HEin0yEsPByGvrEInvFml0uLC4ZWaHashLSiAGWXLvGRR6IeyGAwoKioCB4eHggJCUHv3r15e40cRhAENDc34/r162htbUV0dHSHW298GsUJZWVloa6+HgMSU7tMNABAIvVAUOJilG+ah+zsbKSmpjooyp9w8iqRuKRSKSIjI1FeXt6uayWRI8nlcoSHh9/RHB9WNhxEEARED43FVc9QBE97zeJxml2rEdpSgaLzBQ670Ot0OmRlZWFd5gYUFxWavh4VHYPFC19CcnIyKy1EDiQIAlpaWizqmElkSx4eHvD09Lzt9cfS6zeTDQfRaDQICQlB8PTX4RM73uJxdee+hmbXamg0GiiVSjtG2MbYZKquvh4+MePgHfNTk6nGQjXqCo/ARy5HzrZtnEtCRNTD8TaKkzEuBCT19rVqnHH72tpauycbt05elfoEwtBQA+FGIyS9vCEfOg6BdQs4eZWIiKzCZMNBfH3bkgZDo96qccbt/fz8bB7TzXQ6HWbNng2viJFQ/PLXqDt7CLX5n6Ol8rJpG0/FQPiN/CUUv/w1Kj9/H7Nmz+bkVSIiMot9NhxEqVQiKjoGjYVqq8Y1FKkRFR0DhUJhp8jaGCevyodNwpU/L0DVwc3o3SeyXQ+Q3n0iUXVwM678eQFkwyaivr4e2dnZdo2LiIhcH+dsOFBGRgaWpKVhQMpHHfprdKZFX4nyTfOQvmaNXZ9GMU5evdzYG01XzkEWOQrKyR17gABAq74K2n1r0VByCl4DhmGgd7NDJ68SEZHz4ARRJ2Rtnw3tjpWQOKDPhnHyqqSXF7zDhyNkpvnYrv9jJRovfQvhRpPDJq8SEZFz4dooTigwMBA527ahqTQfmh0r0aKv7HS7Fn0lNDtWorE0H9tzcuw+J8I4eVVobYFysmU9QBSTF0NobQHQNnmViIjodjhB1MESExOxZ/duzJo9G+Wb5kEWEw9Z9E+PlzYUqdFQqIZcLsfePXuQkJBg95h8fHwAiRTymHiLbu8AgKevAvLosagvVJsmvxIREXWGyYYIEhMTUXbpErKzs7F2fSaKd602vRYVHYPUNWuQnJyMgIAAxwUlGCAfOs6qIfKh41B//j92CoiIyPmws3L38DaKSAIDA5Gamoqi8wXQaDQoKSmBRqNB0fkCpKamOjTRqKurA9D9HiDG2zBERO5Kp9MhIyMD0UNjERISgsjISISEhCB6aCwyMjKg0+nEDtGpsbIhMolEAqVSKeoES2fvAUJEJKZbOysHT59puvVdUajGkrQ0vLl8OTsrd4HJRje4WxnN2APkauERq1qpNxQ6pgcIEZFYbu2sfOu8Np/Y8QjUV7Gzshm8jWIFdy2jSSQSLF74EuoL1WjVV1k0pkVfiYYiNVIXLXTpRIuI6HZu7qwcPOPN206g9/ANQvCMN+EVMRKzZs922WuBPTHZsJBKpUJYeDiWpKWhwjO0XWfNCs9QLElLQ1h4OFQqldihdktycjJ85HJUqtZCMHS9sqRgaIVOtQ5yuRxJSUkOipCIyLGMnZUViZa1BAhKXMzOyrfBpl4WuLmMpuikjAa0ddasVK1FU2m+y5bRbj7OoMTF8PTteHukRV+JKtU6NJXmO+zRXCIiRzN2Vr7qGYrgaa9ZPE6zazVCWyp6TGdldhC1EWu7fmp2rITUAV0/7cU4Eaq+vr7LHiDbc3KYaBCR2zJ2Vg6e/rpVc9nqzn0Nza7VPaazMjuI2khPK6MZe4Ckr1mD0JYKaHatxrXPlpuy9fQ1a3C5rIyJBhG5NeMj/d1tCcDOyu2xstGFnl5GEwQBlZWVqK2thZ+fHxQKhUsfDxGRpVjZsAwrGzag1WpRXFQIWUy8VeNk0fEoLipEZWXna5+4CmMPkIiICJd/vJeIyBrGlgCNhWqrxjUUsSVAZ5hsdIFlNCKinsnYEqCu8Ih1LQEK2RKgM0w2usDOmkREPRdbAtgOk40usIxGRNRzBQYGImfbNjSV5kOzYyVa9J3fGm/RV0KzYyUaS/OxPSfHJZ9EtDe2K++CsYy2JC0Ngfoqi5ZfN5XR1qxhGY2IyMUlJiZiz+7dmDV7Nso3zeuyJQB7D90en0Yxw9o+G9odKyFx4T4bRETUkU6nQ3Z2Ntauz0RxUaHp61HRMUhdtBDJyckOXa3bWbCplw2xsyYREQFsCXArJhs2xs6aRERE7THZsAOW0YiIiH7CZMOOWEYjsj1BEKDVaqHX6+Hr68tGckQugB1E7YidNYlsR6fTISMjA9FDYxESEoLIyEiEhIQgemgsMjIyoNPpxA6RiO4QKxtEJBrjXKi6+nr4xIyDd8xPc6EaC9WoKzwCH7kcOdu2ITExUexwiegWll6/bdpno7y8HE8++SQyMjIwYsQIW+6aiNzMzU95DUhM7dDHxid2PAL1VahUrcWUqVOxZ/duJhxELspmt1FeeOEF9O/fH//+979ttUsiclM6nQ6zZs+GV8RIBM9487YN8zx8gxA84014RYzErNmzeUuFyEXZLNl45513UFJSYqvdEZEby8rKQl19PRSJqV02ygMAidQDQYmLUV9fj+zsbAdFSES2ZLNkIzg4GBERERZt29TUhJqamnYfRNQzCIKAdZkbII8ZZ9ESAADg6auALCYea9dnwgmmmRGRlUR5GmXVqlUICAgwfYSFhYkRBhGJQKvVorioELKYeKvGyaLjUVxUiMrKzhfDIiLnJUqysWzZMlRXV5s+ysrKxAiDiESg1+sBAFJvX6vGGbevra21eUxEZF+irPrq5eUFLy8vMd6aiETm69uWNBga9VaNM27v5+dn85iIyL7Y1IuIHEqpVCIqOgaNhWqrxjUUqREVHQOFouNCiETk3JhsEJFDSSQSLF74EuoKj6BVX2XRmBZ9JRoK1UhdtJAde4lcEJMNInK45ORk+MjlqFSthWBo7XJbwdAKnWod5HI5kpKSHBQhEdmSzZMNQRDYPZSIuhQYGIicbdvQVJoPzY6VaNF3/oRJi74Smh0r0Viaj+05OQgMDHRsoERkE1wbhYhEY1wbpb6+HrKYeMiif1obpaFIjYZCNeRyObbn5CAhIUHscInoFlxinohcgk6nQ3Z2Ntauz0RxUaHp61HRMUhdtBDJyckICAgQMUIiuh0mG0TkUgRBQGVlJWpra+Hn5weFQsHJoEROTpRVX4mIuksikUCpVEKpVIodChHZGJ9GISIiIrtiskFERER2xWSDiIiI7IrJBhEREdkVkw0iIiKyKyYbREREZFdMNoiIiMiumGwQERGRXTHZICIiIrtiskFERE5DrVZj7LjxUKvVYodCNsRkg4iInMa6detxTH0E69dnih0K2RCTDSIicgoajQbbt2+Hp2IgcnJyoNFoxA6JbITJBhEROYWsrCy0CgL6zHwDrYKA7OxssUMiG+Gqr0RE5HBXrlxBRUVFu69t2PRnyGLi0UsZBln0WGRu3IRJkya126Zv374YMGCAAyMlW2CyQUREDjfnmWfx9aGD7b4mkXqgz5wFAADfUVNQ8vEyjB49ut02E/7rZzh84CuHxUm2wWSDiIgc7rl5c5F7/DhuCBIEPpyCXsowSL190SswFADgPfAu9Hv+Axga9bihLYNu/yb0kgiYPzdZ5MipOzhng4iIHC4pKQl5J3MxJCIcui8ycUNz0ZRoGPUKDMWN6xeh+yIT0ZGDkHcyF0lJSSJFTHeCyQYREYkiLi4OeSdzMWvGf0O7Jx0t+sp2r7foK6Hdm47ZM2cg72Qu4uLiRIqU7hSTDSIiEo2Pjw8mTpwAidQDHt6+AABDUz0AwMPbFxIPD0ycOAFyuVzMMOkOMdkgIiJRnTx5Ep6KARAMBmj3vo+y9x+Hdu/7EAQDvEMGIS8vT+wQ6Q5JBEEQxA6ipqYGAQEBqK6uhr+/v9jhEBGRA/UN7YfqXkoIDdWQ1lfixZQXsHHTnwG/PhB6yxEd1AvfffuN2GFSJyy9frOyQUREoqmrq8O1igo0Xf4ekSF+yDuZizVr1iDvZC4GKWRounwW5859j8bGRrFDpTvAR1+JiEg0v/rVrwAPDwy/Kw5Hjx41zc0wTh5dtGgRTn/Xlmx4e3uLHC11F2+jEBGRKBoaGkzJhcFggEQi6XQ7g8EAqZSFeGfE2yhEROTUHn74YQDApk2bbptoAGCi4QZY2SAiIofTarUIDg4GADjBZYi6iZUNIiJyWnfddRcAYPfu3SJHQo7AZIOIiByqpKTEtOLrlClTRI6GHIHJBhEROdTgwYMBALm5uSJHQo7CZIOIiBzm5gTjvvvuEzESciQmG0RE5DBjxowBAPzwww8iR0KOxGSDiIgcYs+ePQCA0NBQREZGihwNORKTDSIicoipU6cCAL7//nuRIyFHY7JBRER2t2nTJgBAfHw8FAqFyNGQo7GpFxER2ZUgCKYuoPX19ZDJZCJHRLbCpl5EROQUli1bBgBISkpiotFDsbJBRER209LSgl69epn+7eHhIXJEZEusbBARkeiefvppAMBbb73FRKMHs1mycerUKYwYMQIymQwJCQm4du2arXZNREQuqK6uDp999hmAtmSDei6bJBsGgwGPPfYYpk6diqKiIshkMixZssQWuyYiIhc1YcIEAMDmzZu7XEKe3J9N5mwcPHgQ//3f/w2tVgtPT0+cOnUK48ePx/Xr1+Hj42N2POdsEBG5l2vXrqFv374AuIS8O3PonI0jR45gzJgx8PT0BACMGDECra2tOHXqlC12T0RELiYmJgYAoFKpRI6EnIGnLXZy9epVBAcHmz6XSqUICgoyLSF8q6amJjQ1NZk+r6mpsUUYRETkBIqKilBdXQ0ASEhIEDkacgY2myB6a5lMEITb3qNbtWoVAgICTB9hYWG2CoOIiERmrGrk5+eLHAk5C5skG/369cP169dNn7e2tkKn0yE0NLTT7ZctW4bq6mrTR1lZmS3CICIiBxMEARqNBqWlpdBoNFCr1abXRowYIV5g5FRschvloYcewurVq9HS0gJPT0/k5+fD09MTI0eO7HR7Ly8veHl52eKtieg2BEGAVquFXq+Hr68vlEolnwggm9HpdMjKysK6zA0oLir86QVJ29+w3333nUiRkTOySWVj/PjxCAkJwVtvvYXLly/jd7/7HWbOnAm5XG6L3RORFXQ6HTIyMhA9NBYhISGIjIxESEgIoofGIiMjAzqdTuwQycWpVCqEhYdjSVoaKjxDETz9dfR5YiWCp78OeUw8IJFibHw8J4eSic3alZ86dQrz5s3D+fPnMWHCBGzduhUhISEWjeWjr0S2oVKpMGv2bNTV18MnZhy8Y+Ih9faFoVGPxkI16gqPwEcuR862bUhMTBQ7XHJBKpUKU6ZOhVfESCgSU+HhG9Rhm1Z9FSpVa9FUmo89u3fze82NWXr95tooRG6CFwGyN51Oh7DwcBj6xiJ4xpuQSG/fflwwtEKzYyWkFQUou3QJgYGBjguUHIZroxC5mFsn2lnzd4BOp8Os2bPhFTESwTPe7DTRAAAP3yAEz3gTXhEjMWv2bN5SIatkZWWhrr4eisTULhMNAJBIPRCUuBj19fXIzs52UITkrJhsEInMFnMseBEgexMEAesyN0AeM+62yeytPH0VkMXEY+36THYR7eF4G4VIRLaYYyEIAqKHxuKqZyiCp71m8Xtrdq1GaEsFis4X8CkVMkuj0SAkJATB01+HT+x4i8fVnfsaml2rodFooFQq7RghicHS67dNHn0lIuvdPMdiQCdzLHxixyPw/+ZYTJk69bZzLLRaLYqLChE8faZV7y+LjkfxrtWorKzkRYDM0uv1AACpt69V44zb19bW8vusB+NtFCIR2HKOhS0uAkTm+Pq2fb8YGvVWjTNu7+fnZ/OYyHUw2SASgS3nWPAiQI6gVCoRFR2DxkK1+Y1v0lCkRlR0DBQKhZ0iI1fAZIPIwWw90Y4XAXIEiUSCxQtfQl3hEbTqqywa06KvREOhGqmLFnJeUA/HZIPIwYxzLGQx8VaNk0XHo7ioEJWVle2+zosAOUpycjJ85HJUqtZCMLR2ua1gaIVOtQ5yuRxJSUkOipCcFZMNIgezxxwLXgTIEQIDA5GzbRuaSvOh2bESLfrKTrdr0VdCs2MlGkvzsT0nhw29iE+jEDmaPeZYGC8CU6ZOhWbHSgQlLoanb8fbIy36SlSp1qGpNB979+zhRYCslpiYiD27d2PW7Nko3zQPsph4yKJ/emS7oUiNhkI15HI59u7Zg4SEBLFDJifAPhtEDmbsi1HhGQqljftiGPt21NfXd3kR2J6Tw4sA3RGdTofs7GysXZ/ZbtXXqOgYpC5aiOTkZAQEBIgYITkC10YhcmIZGRlYkpaGASkfWTRJtEVfifJN85C+Zg1SU1O73JYXAXIkQRBQWVmJ2tpa+Pn5QaFQcB5QD8Jkg8iJWbuglXbHSkisXNCKFwEisjcuxEbkxBwx0U4ikUCpVCIiIgJKpZKJBhGJhpUNIhFxjgURuTLeRiFyEZxjQUSuiskGkYvhHAsicjVc9ZXIxRjnWHBlTCJyN5wgSkRERHbFZIOIiIjsirdRSHSCIECr1UKv18PX15ePaRIRuRlWNkg0Op0OGRkZiB4ai5CQEERGRiIkJATRQ2ORkZEBnU4ndohERGQDfBqFRGHsL1FXXw+fmHHwjvmpv0RjoRp1hUfgI5cjZ9s2JCYmih0uERF1gk+jkNNSqVSYMnUqvCJGYkBiaoe1QXxixyNQX4VK1VpMmToVe3bvZsJBROTCWNkgh7J2TRDNjpWQWrkmCBEROQbXRiGnlJWVhbr6eigSU7tMNABAIvVAUOJi1NfXIzs720EREhGRrTHZIIcRBAHrMjdAHjPOomXVAcDTVwFZTDzWrs+EExThiIioG5hskMNotVoUFxVCFhNv1ThZdDyKiwpRWdn5yqhEROTcmGyQw+j1egCA1NvXqnHG7Wtra20eExER2R+TDXIYX9+2pMHQqLdqnHF7Pz8/m8dERET2x2SDHEapVCIqOgaNhWqrxjUUqREVHQOFQmGnyIiIyJ6YbJDDSCQSLF74EuoKj6BVX2XRmBZ9JRoK1UhdtJAtzImIXBSTDXKo5ORk+MjlqFSthWBo7XJbwdAKnWod5HI5kpKSHBQhERHZGpMNcqjAwEDkbNuGptJ8aHasRIu+8ydMWvSV0OxYicbSfGzPyWFDLyIiF8YOoiQK49oo9fX1kMXEQxb909ooDUVqNBSqIZfLsT0nBwkJCWKHS0REnbD0+s1kg0Sj0+mQnZ2NteszUVxUaPp6VHQMUhctRHJyMgICAkSMkIiIusJkg1yGIAiorKxEbW0t/Pz8oFAoOBmUiMgFcNVXchkSiQRKpRJKpVLsUIiIyA44QZSIiIjsiskGERER2RWTDSIiIrIrJhtERERkVzZLNsrLyzFx4kR88803ttolERERuQGbJBsvvPAC+vfvj3//+9+22B0RERG5EZskG++88w5KSkpssSsiIiJyMzbpsxEcHIzg4GBb7IqIiIjcjMWVjeTkZAQGBnb4+MMf/mD1mzY1NaGmpqbdBxEREbkni9uVX79+HXV1dR2+bkw6gLZOkPn5+RgxYkSX+1qxYgXefvvtDl9nu3IiIiLXIcraKJYmG01NTWhqajJ9Xl1djfDwcJSVlTHZICIichE1NTUICwuDTqfrcuFMUdZG8fLygpeXl+lz422UsLAwMcIhIiKiO1BbW+t8ycat+vfvj7KyMvj5+dlttU9j9uWu1RN3Pz6Ax+gu3P0Y3f34AB6ju7DFMQqCgNraWvTv37/L7Zwi2ZBKpRg4cKBD3svf399tv3EA9z8+gMfoLtz9GN39+AAeo7u402PsqqJhZNNkw4bTP4iIiMhNcG0UIiIisqsek2x4eXnhrbfeajcx1Z24+/EBPEZ34e7H6O7HB/AY3YUjj9Gmj74SERER3arHVDaIiIhIHEw2iIiIyK6YbBAREZFduU2y8d1332Hs2LHw8/NDYmIiLl26ZHaMSqVCTEwMfHx88OSTT6K+vt4BkXZPd45PIpG0+4iIiLB/oHdArVbjnnvugVwux5NPPomGhgazY1zpHALdO0ZXOI/l5eWYOHEivvnmG9PXuntu3nvvPfTp0wcKhQIrV660U8TWs9UxHjp0qMM5nTt3rv0Ct0JnxwgAFy5cQFxcHHQ6ncX7cqXzCFh/jM56Hjs7vry8PIwaNQr+/v6YPn06KisrLdqXTc+h4CaioqKElStXCleuXBGef/554ec//3mX21dVVQn+/v7Cxo0bhZKSEuGBBx4QfvOb3zgoWutZe3yCIAgAhHPnzglVVVVCVVWVUF1d7YBIu+fGjRtCWFiYsHz5cqG4uFiYMGGC8Nprr3U5xtXOYXeOURCc/zw+//zzAgABgJCfny8IQvfPzYEDBwR/f3/h66+/Fk6cOCEEBQUJX3zxhZ2PwDxbHuPBgweFqKgo0/msqqoS6urq7HwE5nV2jIIgCAkJCaavV1VVWbQvVzqPgtC9Y3TG89jZ8bW2tgoxMTHC0qVLhZKSEuHBBx8UFi5caHZftj6HbpFsXLt2TQAglJeXC4IgCGq1WpDL5V2O2bx5sxAXF2f6fPv27UJ4eLhd4+yu7hyfILRdpJqamuwdnk2cOXNGACBUVlYKgiAIe/fuFcLCwroc40rnUBC6d4yC4Pzn8fr160JJSUm7X3DdPTdJSUnCSy+9ZPp88eLFwrPPPmvzmK1ly2M8ePCgEB8fb69Qu62zYxQEQSgvLxe+/vprqy7ErnQeBaF7x+iM57Gz4ysqKhIACHq9XhAEQcjMzBSGDx9udl+2PoducRslKCgIAwcOhEqlAtBW2jS38uyRI0cQHx9v+vyBBx7ApUuXUFZWZs9Qu6U7xwcA3t7eeOaZZyCTyTBhwgRcuXLFzpF2n/F2gkwmAwAEBwejrKwMer3+tmNc6RwC3TtGwPnPY3BwcIdbO909N52NO3LkiE3j7Q5bHiMA6PV6jB49Gn5+fvjVr36F5uZmW4dstc6OEQBCQ0OtXk7Clc4j0L1jBJzvPHZ2fHK5HO+//z58fHwAAFqt1vQ7qCu2PodukWx4enris88+wwsvvAAvLy+sW7cO2dnZXY65evUqgoODTZ8rlUoAQEVFhV1j7Y7uHB8ANDY24r/+679QWFgIT09PvPrqqw6Itnuio6Mhl8vxz3/+Ey0tLdiyZQsAoLq6+rZjXOkcAt07RsC1zqNRd89NZ+Oc9XzeyfdfWVkZ/vSnP0GtVmPv3r2m7wV34Urn8U64wnns378/Xn75ZQBtv2v+8pe/4NlnnzU7ztbn0C2SjYaGBiQlJeHtt9/GqVOn8Mwzz+BXv/qV2XHCTf3MjP+216qzd6K7x1dWVoYXX3wRYWFhSElJwcGDBx0QbfcEBATg/fffR3JyMgIDA02ZtzEbvx1XOYdA94/Rlc7jzbp7bm4d56znE+jeMT744IMoLCzEpEmTcM8992DmzJkuc06t4UrnsTtc7Tzq9XpMmTIFI0eOxIsvvmjRGFueQ7dINr744gs0Nzdj6dKluOuuu/Dee+9BrVbj22+/ve2Yfv364fr166bPtVotgLZymrPpzvEBaFcWDAwMRE1Njb1DvSMLFixAdXU1ysvLMX36dHh5eSEwMPC227vSOTSy9hgB1zuPQPfPTWfjnPV8dvcYvb292/3F6Crn1BqudB67y5XOY319PRITE+Hv74+///3vkErNX/ptfQ7dItnw8PBAa2ur6XNBEGAwGODpeftFbR966CGo1WrT58eOHUNERAQGDBhg11i7ozvHt2HDBiQkJJg+v3TpklM+Mmmk1+sxefJk6PV6+Pn54cSJExg3blyXY1zpHALdO0ZXO49G3T03nY0bP3683eK8E909xieffLLdY4Suck6t4Urnsbtc6TwuWLAAvr6++Oc//2nxOig2P4fdnlrqRK5duyb4+fkJa9asEcrKyoS0tDRh0KBBQnNzs1BdXS00Nzd3GKPT6YSAgABhw4YNQklJiTBmzBjhzTffFCF687pzfN99953g5eUl/Otf/xIKCgqEu+++W1i9erUI0VsuNjZWWL58uXDu3Dlh8ODBwt/+9jdBEAS3OIdG1h6jK51H3DQDvqtz09raKlRVVQkGg6HDPg4ePCj4+fmZHrcLDAwU9u/f78jD6JItjnHDhg1CRESEcPr0aeHAgQOCr6+vcPz4cUceRpdwy5MagiCYnnC4+UkNdzmPRtYeozOfx5uP7z//+Y/g7+8vFBYWtntMVxAcew7dItkQBEHYt2+fcPfddwtyuVx44IEHhLy8PEEQBGHQoEHCjh07Oh2jUqmEIUOGCDKZTHjiiSdEf0a6K905vi1btgjh4eFCSEiI8Otf/7rTi5kzyc3NFWJjYwV/f/92/Sfc5RwKQveO0VXO462/wG93bjr7pX6z9957T1AqlUJQUJCwcuVKB0RuOVscY0tLi/Dyyy8LCoVCiIyMFDZv3uyg6C1j6YXYnc6jIFh/jM58Hm8+vhUrVph6b9z8IQiOPYdc9ZWIiIjsyi3mbBAREZHzYrJBREREdsVkg4iIiOyKyQYRERHZFZMNIiIisismG0RERGRXTDaIiIjIrphsEBERkV0x2SAiIiK7YrJBREREdsVkg4iIiOzq/wMPIh/Xkz6VSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 只考虑一个近邻\n",
    "mglearn.plots.plot_knn_classification(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b34f1d18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 考虑多个近邻\n",
    "mglearn.plots.plot_knn_classification(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "01dcb879",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# 生成模拟数据集\n",
    "X, y = mglearn.datasets.make_forge()\n",
    "\n",
    "# 将数据集分为训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cf550691",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 实例化 KNeighborsClassifier 并设置邻居个数\n",
    "\n",
    "clf = KNeighborsClassifier(n_neighbors=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fad0bf22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练数据\n",
    "\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "27495f48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set prediction: [0 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "# 预测出现次数最多的类别\n",
    "\n",
    "print(\"Test set prediction: {}\".format(clf.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d9f28c54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "# 预测精度\n",
    "\n",
    "print(\"Test set accuracy: {:.2f}\".format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a801334",
   "metadata": {},
   "source": [
    "### 决策边界可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "07ef6250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27c7e0a2450>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7MXfuXHz99dd4++238fLLLwOoPbN6//3349FHH0VNTQ2Sk5Px1FNPoVu3bs3OKNiSOXPm4LHHHsP+/fvrLtcoKCjA+vXrsWjRIiQkJOC1115DREQEUlJS6vZbuXIlOnbsWHcARCRlcXFxWLRoEVJSUlBUVITXXnutwb+dvsAMYYb4VXBvoQkd3tx037FjxwaP2W9+s6upqRHz588XKSkpQq/XiyFDhogDBw40ea3mbrq32WzirrvuEvHx8UKv1wsAAoDIyMgQFy9eFELU3nTf+Ma5YcOGifnz5wsham+6HzNmjBg3bpzQ6XSic+fOYs2aNQ22Ly8vF5MmTRKxsbEiNjZWPPTQQw1u4rfXd/ToUZc+m6qqKtGlS5cGdZ09e1bce++9Ijk5Weh0OjFw4ECxd+/euue3bdsm2rRpIwoLC116DyIpqKioEL///e9FmzZtRGJiohg/fry4dOmSy/s3zgwhHOdBbm6u6NSpk9BqtaJfv37i448/dvh6aOaG2T/96U8iNjZWREdHC5VKVTeJxnfffSeEaJgZdhMmTBATJkwQQtTeMJuZmSmmT58uoqKiRPv27cXixYvrblwVQogrV66I2bNni8TERKHX68XYsWMb3GBrr2/z5s0ufTY2m00MHjy4QV3l5eViypQpol27diIyMlL07t1bfPjhh3XPHzlyRCQnJ4v8/HyX3oMo2CorK8UDDzwgYmJiRPv27cWqVavc2p8Z0jxmSGCwYSGxZs0akZaWJjZv3iz27t0rdu/eLZYvXy7Cw8MbfMGk6NtvvxWJiYni+++/b3HbsrIykZycLHbt2hWAyohCy7Zt20RcXJzYsGGD2Lt3r9izZ49Yt26diIuLE6+++mqwy3PqwoULomPHjmLnzp0tbltTUyO6du3aYOYiIvIeM4ScUQlRbzUdCkkXLlzAn/70J+zevRvFxcXQaDTo3Lkz7rrrLjz11FMtXi8abD///HODGUN8tS0Rua6yshKPPvooPvvss7pF2Nq3b49Ro0bhmWeegcFgCHKFzp09exYpKSlNZvFxhDlC5HvMEHKGDQsREREREUkWZwkjIiIiIiLJYsNCRERERESSxYaFiIiIiIgkiw0LERERERFJVkCnf7LZbDh//jyio6NdmkWBiPxDCIHKykq0a9fOrQW5go0ZQiQdzBEi8parORLQhuX8+fNo3759IN+SiJw4c+YMUlNTg12Gy5ghRNLDHCEib7WUIwFtWKKjowEAZ5YtQ4xOF8i3JqJ6Kqqq0H7mzLrvpFwwQ4ikgzlCRN5yNUcC2rDYh15jdDrE6PWBfGsickBul0MwQ4ikhzlCRN5qKUfkc9EpERERERGFHDYsREREREQkWWxYiIiIiIhIstiwEBERERGRZLFhISIiIiIiyWLDQkREREREksWGhYiIiIiIJIsNi4R9fuwY+sx9HJ8fOxbsUohIhpghROQt5ghJARsWiRJCYN577+KbM+cw7713IYQIdklEJCPMECLyFnOEpIINi0R9dvQoDvzwI/44MAIHfvgRnx09GuySiEhGmCFE5C3mCEkFGxYJEkJgwfsbMDA1HC/dHImBqeFY8P4GntkgIpcwQ4jIW8wRkhI2LBJkP6OxYFg4VCoVFgwL55kNInIZM4SIvMUcISlhwyIx9c9o3NxZAwC4ubOGZzaIyCXMECLyFnOEpIYNi8Q0PqMBgGc2iMhlzBAi8hZzhKSGDYuEODqjYcczG0TUEmYIEXmLOUJSxIZFQhyd0bDjmQ0iagkzhIi8xRwhKWLDIhHOzmjY8cyGPHCRLQoGZoiyMEcoGJgjyqG0DGHDIhHOzmjY8cyG9HGRLQoWZohyMEcoWJgjyqDEDGHD8j/B7ERdOaNhd3NnDa5vp8Gf/vkPRfwBKg0X2QpdzBDyFeZI6GKOkC8oMUPYsCD4nagrZzTsVCoVnsmOxLfnL+DTI0cCVCG5gotshS5mCPkKcyR0MUfIF5SaIWHBLkAK6neiLx+o7URv6d07IO9t/8PqnKBBol6Fry5YW9wnUa9CepwKj7y2FrE6LV56YAJuuuaaAFRLztj/jraO19cNmY9cH9i/JwoOZgj5CnMkdDFHyBeUmiEh37A07kT3n639+eZrr23xDIMvVNfU4GzJZZwttaL/GpNb+0ZqSnD6MjDvvXdxY0ZGQOolx1paZCtQf08UeMwQ8hXmSOhijpAvKDlDQr5hCXYnGhkejryFz6CooqLJc3mnTiHn73/HfdeG4a2jNQCA3Fu1yGqvwWtfVeNvX1wNypkYaqrx3xEARZ3ZoOZJOUOAX3IkO02DHT9ZmSESxhwJXVLOER6LyIeSMySkGxapdKLtW7VC+1atmtQ2/Y3XMTA1HP/8TSQ+OmlCt1ZqTL8+HADw1QUrrm+nDsqZGGrI1UW2+P9HeaScIfb6pr/xOm5ICUPVVRuub8cMkSrmSOiSco7wWEQ+lJ4hIX3TvaMbzKQyXV/92rb9aEP5FYFnsiOhUqnw2Q9WHDhnwzPZWsnUG8q4yFboknKG1K/v9m6aBpnBDJEe5kjoknKO8FhEPpSeISHbsLjaiQZjVoX6tf2qkxoLdl3BwBQ1bu6sqX1u1xUMTNU4PBMj91kg5IaLbIUuKWdI/fpuSAnDlpNX6zKDGSI9zJHQJeUc4bGIfIRChoRswyLlTrTxGY0DZ61YMLzemdGzViwYFim5MzGhiItshS4pZ0j9+uyjK/bMYIZID3MkdEk5R3gsIh+hkCEh2bBIuRN1eEbDyZnRYNcbytxdZIv/f5RDyhlSvz5XRlekUG8oY46ELinnCI9F5CNUMkQxDYs7q8NKuRN1eEbDyZnRYNcbytxdZIv/f6RNKRlSvz5XRlekUG8oY44oi1JyhMci8hEqGaKIWcIarw7rbB5wTzvRQMyq0PiMxqA3qlw6oxGsekOZp4tsdU7Q8P+PBCklQ+rX587oSjDrDWXMEWVRSo7wWEQ+QilDFNGwuLM6rKM5qpsT6Lmr69dmP6Nhr/PT72sa/CyFekOZN4tsVeMyqmtqEBke7qfqyF1KyZD69S3OjsTTO2qYIRLGHFEWpeQIj0XkI5QyRPYNizurw0q5E3V0RqNzvAqJehW+PF+Dx7dZcMP/Zudwhmc2AqOlxfqcSY6JkU1AhAKlZEj9+uyjK8wQaWOOKIdScoTHIvISShki+4bFndVhpdyJ1v89rtpUOFthw9kK0aBOKZ6JCWXNLdZH8qKUDAF++V023avD7z+2MENkgDmiDErJER6LyE+oZIisGxZ3V4et34nmnTqFnL//Hbm3apHVXoO8M1bkfGJB7oQJyOratcl7+bMTbXy2Jb/IhlWjtCi1CAghsGhXNawCkjsTQyR33mQIALdyxN9ns+rnSLtoNTOEKEB4LOIYc4R8SdYNi6NrQFvq6tu3aoXUhARMf+N1DEwNx/Tra2dV6NtGjfVHrVi/dw+m/+pXAf1itXS2JVwNXLVBcmdiyL8+P3YMs9/8B168/3e46Zprgl2OInmaIe1btYIQQjY5wgwJXcwR/+OxSAuvyxyRNalkiGwbFldXh3XU1XsSLv7U0jWIF8vKUGo2O3wuwWBA69hYh8/J7fpE+oU7s82QZ7zJEEBeOeIsQ4Dmc4QZIm/MEf/jsUgtHosok5QyRLYNi7MZNpx94b09SPEXuVyDKJVOW+ncmW2GPONphgDMEW8xRwKDOeJ/PBYJHuaI/0kpQ2S5cKQ3q8M6W2BHzgvqBELjTltuq6TKRePZZuS6Kq2UebvCNHPEc8yRwGCO+B+PRYKHOeJ/UssQWTYsnq4O6+1BSqir32kzSP2n8d83/+HyPW9WmGaOeIc5EhjMEf/jsUjwMEf8T2oZIruGxdPVYYUQXh2khDqpddpK1dJsM/y8vedNhgDeNTuhjjkSGMwR/+OxSPAwR/xPihkiu4bFlS+6Xf0v/KdHjnh1kOLM58eOoc/cx/H5sWNu/S5yIrVOW6kc/X3z8/YtTzPks6NHvW52mhMKGQIwRwKFOeJ/PBYJHuaI/0kxQ2R10703q8P+6c1/4LvzF32+4JGUZlDwF3fnmCfPSPUmTCXxdoVpIUSzN9g25mqOhEKGAMyRQGGO+B+PRYKHOeJ/Us0QWTUs3qwOq48oQqd4tc8XPJLSDAr+IrWpF5XKm1mryDXerjA934tmp6XpkZWcIQBzJFCYI/7HY5HgYY74n1QzRFYNS0tzhH91uMzh41dtVvz509X4sdTk0wWPGl9Huf+sUFyHL9VOW2ncvQmTn7dnWsoQZ2L1egx/ZqHHzY6jHAmFDAGYI4HCHAkMT3OkuqYGd778En4sreSxiAeYI/4n5QyRTcOye09xvZ+iHW7TLSkax/uOavK4BsAKQyzKqowNHu/XN67F93W24FHjLlRqHb4v5iiXaqetNM4+Zzt+3r7hzToDnjY7zeVIKGQIwBwJFOZI4HiaI/995lmfZgjAHAH4d+0rUs4QSTUsDZuSphw1I80RQsBkKoXFYoZWq4cYfH+DD7/H4S0wnv1l+6FDEt2qVerXUfrielYpd9pK4umN3Py8/UsIgctGI4wWC6K0WrSKivLpomqhkCH212GO+B9zRJoa50hqQoJPF2ZkjvxCSr+3HEk9Q4LSsOzbXwJDhNnhc+40JY6YzRXYv/8D7Ni1HkWXCuoeT2qdjuxh45GZORZ6fUzT99mzBYDrjYvUr6P0xfWsUu60lcSVz9mOn7f/lZlM+PuePcjd9jl+uHih7vHObdoi51c3YcKQIYgzGLx+n1DIkPqvwxzxL+aItDBHGtbHHJE+qWeISgRwMuWKigrExsbilVe+hk7n+LIub+Tn78aq1Tmorq6CvlsWdN0HQa2Ngs1iRNWJfTCfzENEhA5Tp+QiI2Nok/17HN7iUsMihEDW/D8DV84gb5K2wf9YIQSyXrcAke2Rt3BRUDr8xvV5Uo+z39HhtkH+neXK/jkXlZ7Gu2O1cOWjEwIY94EFSfEdPf68K8xmxE6ejPLycsTExHhQeXDYM6R8zRrE6PU+f/1PjxzBXcuWwXSlGoZuWdDWyxDLiX0wncyDITIC78+c6VVAh0KGOHod5oh/MEfcwxwJDOaIfAQrQwDXc0RSl4R5Iz9/N5Yvnwxtel+kjJwJTVR8g+cNPQbDaixFydZlWL58MmbMWNOkaTnedxSwp+WmRerXUfrielapd9pK4e2sVc3dhEnu+/TIEdz2wouITO+LlJGzHGZInLEUJVtfxW0vvIiPHpvt8d96KGSIo9dxRgq/t1wxR6SDOdJ8fcwR6ZJDhihihMVsrsDcJ4ZAk9ITiWOehkrd/LV3wmZF8cbFsJ77DkuX7IFe37Cba2mUxZVOP5gdvqP63K0nmJ12KDpz+bLHN2GmengtNM+MNlRmMqH9rFmwtctA4ph5LmTIM1Cfz8eZV191+7KOUMiQ+q/DHAkM5ojrmCP+xxyRn2BkCBBiIyz793+A6uoqpIyc6TQgAECl1iB+ZA7Or3gQBw5sxIgRE5tss3tPcbNNi9Svo/TF9axy6LSVxJc3cpNn/r5nD0xXqpEycpaLGTITF1Y8iH/s2YOZI0e69V6hkCEAcyTQmCPBxxxxXh9zRNqkniGyb1iEENixaz103bKaDL02JywqAfpuWdi+801kZ09o8GU/3ncUehze0ux7SXkGBV/NUe7NWhXOpl4kkiIhBHK3fQ69mxmi65aJZds+R84tt7h1b1goZAjAHKHQwhxxrT7mCHlK9g2LyVSKoksFSBw4zq39dN2yULTpOZhMZYhyEC6ORlmkfh2lL69nlXqnTeQrl41G/HDxAhJvmODWfrpug/DDpr0oMRrRKtq1S1xDKUMA5giFDuaIa/UxR8hTsm9YLJba6ZHV2ii39rNvb7GYmjQsjkZZ7GcMOidokKhX4asL1hbfI1GvQucETUDObHCOciLPGC0WAJ5nSKXF4tKBBjOESLmYIw3rY46Qr8m+YdFqa2+Ys1mMLWzZkH17rbb5G93qj7JI/TpKqV/PSiRVUVotAM8zJPp/+7eEGUKkXMyRWswR8hfZNywGQzySWqfDfGIfDD0Gu7xf1ck8JLVOh8EQ5/D5xqMsUr6OUurXsxJJWauoKHRu0xaX3M6Qfejcpi0Solw7o8oMIVIu5ghzhPxL9g2LSqVC9rDx2PD+EliNpS7d7FZjLIH5ZB5G3f1ki1+S+qMsUr2OUurXsxJJmUqlQs6vbsIf17+FODcypOrkfswcf59b/9AyQ4iUiTnCHCH/Uge7AF/IzByLiAgdSrYug7A5v55T2Kwo3ZqLiAgdBg4c43Tb431H+bJMv3B0PWtL/9W/njWAy/AQSdaEIUNgiIxAydZXXcqQsq3LoI+MwO+GDAlQhf7DDCHyDeYIc4T8R/YjLACg18dg6pRcLF8+GcUbFyN+ZA7CohKabFdjLEHp1lxYCg4jJ2dtk0Ujm+NsXZZgk/r1rERyEGcw4P2ZM3HbCy+ieOMziB8500mGLMOVgsP4+LHZbi/2JkXMECLfYI4wR8h/FLHSvV1+/m6sWp2D6uoq6LtlQdctC2ptFGwWI6pO5sF8Mg8RETo8MnU5evVy/YxGj8NbJNuwAMFbnZTkiytUO/bpkSO4a9kymK9UQ9ctE7pug+plyD5UndwPfWQEPpg5Ezcr6BIGZgh5gjniGHPEPcyR0BZSK93bZWQMxdIle3DgwEZs3/kmijY9V/dcUut0jLr7SWRmjvWoWZLyKItUr2clkptbevfGmVdfxT/27MGybZ/jh017657r3KYtZo6/DxOGDkWsHw5ygokZQuQ7zBEi31NUwwLUXh42YsREZGdPgMlUBovFBK3WAIMhzuNZKByty0JEyhRnMGDmyJHIueUWlBiNqLRYEK3VIiEqijPZEJFLmCNEvqW4hsVOpVIhKire4Sr2npLyKAsR+ZZKpUKr6GiXV58mImqMOULkG4qYJSwQ5DBjGBERERGR0rBhISIiIiIiyWLD4objfUdh957iYJdBRERERBQy2LAQEREREZFksWFxE0dZiIiIiIgChw0LERERERFJFhsWD3GUhYiIiIjI/9iweIBTHBMRERERBQYbFi9wlIWIiIiIyL/YsHiIoyxERERERP4XFuwC5G73nmIMHZIY7DKISIKcjcIyN4iIiFzDhsULx/uOQo/DW4JdBhEFUUuXhjY7GrunYXawgSEiInKMDYsPcJSFSJlcuU/N08tD6+/X4/CWBu/FPCEiIvoFGxYvcZSFSN48HiHxoSbvwdEXIiKiOmxYfISjLETy4KhBkdokGo5GX5gvREQUqtiw+IB9lMXdaY55AELkX819J6XWoDjDUVwiIgp1bFh8xN0DIEcNDhsYItfs218CQ4TZpW3l1JwQERFRU2xYgqTxQRRvuiVy3cnet0Cniw52GURERBQAbFgkwtmMQQAbGCIiIiIKTWxYJIijL0RERP5V/9JS/rtKJG1sWGSA6zUQERH5lv3SUin+u+ruJD71SaF+Il9jwyIzXK+BiIjId4J5SbazxsSTCUMcTYPOadFJCVxqWI4cOeLSi/Xu3durYsh9HH0huWCOEJE3ApEhLV2Sbdfcv6/ujoz4ehbD5pZZ4LEByZ1LDcvMmTOxZ88eCCGa3UalUsFqtfqsMHJfc6MvDCeSAuYIEXkjGBniqKFoad21YE+l3uL7++DKjOZ+fx5vkL+41LC8/fbbSElJwU8//YQOHTr4uybyES44R1LCHAkMHkiQUkklQ4LdkHirpUvgPHkdd16LWUSecKlhadu2LaZMmYKoqCh/10NECsUc8Q9HBwjNHUjwQIHkjBnie75svlx5rZaaGmYUNcflm+5XrlzpzzqIKAQwRzxnbzhcaVAcPc+mhZSAGSJvrl6uxpyixjhLGBGRxNVvODw9I9rczbiBwIMPInKFpydXOKmA8rFhISKSAV9cuhG0a+85/ToRucjTkyue7sc8kgc2LERE5FdKmH49kGtzEIU6b0aS3cFLZeWDDQsREQVMMBfpc1fj2uy18yCHSBncHZXhdz542LAQSYQ9MBmIFCpamhY1GN+F5poUh4/xBmEi2XNrVKbe5a383gcWGxYiCah/kMTrbylUNbf4rZ2//tZdaVIcaXx2lt9FImXjKGvwsGEhCqL6B0qeXLPr6axPpmqL2/sQBZo/733xtElpjAcwRKGHJysCz+OG5eLFi0hKSsLly5eRlJQElUrly7rIB7jKvbTZg86bmZs83beqqhLAHI/f11eYI+QqX4y++KpJcYSXiQUHM4SCpUF+8Hvvd2p3d/jiiy/QtWtXpKam4ty5c1i4cCE6deqEo0eP+qM+8hK/PNLki2ZFzpgj5K3jfUfV/QfUfqfs/zVW//HG+/mjrsbvSb7HDCEp4ffe/9xuWKZNm4YZM2YgOjoaALBs2TJMmjQJU6ZM8XlxREoU6s0KwBwh33LWvDRuVAJZj70W8j1mCEmNowwi33G7YTl16hTGjBlT97NGo8GDDz6Ib7/91qeFESkRm5VazBHyl/rNi79HU1ypBeDBiz8wQ0iqeMLCP9y+h+X666/Hzp07oVKpoFKpYLFYsHTpUvTv398f9REpAhuVhpgjFCp4U75/MENI6nhfm2+53bCsXLkSd9xxB8rLyzF8+HAUFhYiJSUFmzdv9kd9RLLHZqUp5giFGh68+BYzhOSC333fcLth6dy5M44cOYKDBw/i7NmzSE1NxYABAxAWxhmShRAwmUphsZih1ephMMQHfcYSntELLjYrjjFHHJNihpBv1Z8OldnsOWZI85gj0sSpkL3j0Tdbo9EgKyvL17XIltlcgf37P8COXetRdKmg7vGk1unIHjYemZljodfHBLwu/sMYXGxWnGOO/EKqGUL+wTOuvsEMaYg5In2NLxEF+P13lds33T/11FP46aef/FCKPOXn78bcJ4Zgw/tLYI5PReLouUgetxiJo+fCHJ+KDe8vwdwnhiA/f3dQ6pPCjV+fHzuGPnMfx+fHjgWthkBjs+Icc+QXUs8Qqfjuu314dtEt+O67fcEuxWd4U77nmCENMUdaJqUM4Yxi7nN7hOXjjz/G9ddfj7S0ND+UIy/5+buxfPlkaNP7ImXkTGii4hs8b+gxGFZjKUq2LsPy5ZMxY8YaZGQMDXidwRxpEUJg3nvv4psz5zDvvXdxY0aGooem2ai4hjlSSy4ZEmxCCGz+8EX8fO57bP7wRfTokaWYHOFN+Z5hhvyCOdIyqWYIR1xc5/YIy9KlS7Fw4UKcOXPGH/XIhtlcgVWrc6BN74vEMU83CQg7TVQ8Esc8DW16X6xanQOzuSLAldYK1kjLZ0eP4sAPP+KPAyNw4Icf8ZmCF/Vis+I65oj8MiSYvv12D34oOII/DozADwVH8O23e4Jdks/xbKt7mCG1mCOukXqGcCrklrndsLzzzjsICwtD9+7dMW7cOEyaNKnuv1Cyf/8HqK6uQsLImVCpNU63Vak1iB+Zg+rqKhw4sDFAFTYV6C+DEAIL3t+AganheOnmSAxMDceC9zdACBGQ9w8kNivuYY7IM0OCQQiBjza/jBv+lyM3pIbjo80vKzJHeNDiOmZILeZIy+SUIfYM4ImLpty+JCwtLQ1paWkYNSp0D8yEENixaz103bKaPZvRWFhUAvTdsrB955vIzp4QtKHIQF4eZh9d2TpeD5VKhQXDwjFyfe0oyy29e/v1vQOJzYr7Qj1H5JwhgWY/M/rX/+XIwmHhGLm+9gypUi9r4U35LQv1DAGYI66SY4Y0nlGssVDMBLcblvnz5/ujDlkxmUpRdKkAiQPHubWfrlsWijY9B5OpDFEuhos/BKJpqT+6cnPn2rM+N3fW1I2y3HzttbIPyvpBwmbFPaGeI3LPkECpf2a0fo7Yz5D26jVE9jniTOPGBQjNAxVHQj1DAOaIK+ScIU6PK+plQmNKzQi3G5aff/652ec6dOjgVTFyYbGYAQBqbZRb+9m3t1hMQQ8JfzctjUdXAChqlIWjKt4J9RxRQoYEQuMzowBkc4bUlxzdmAso98DEFaGeIQBzxBVKzZDmjj2UPHmHR5eEqVSqumv/6nemVqvVd5VJmFarBwDYLEa39rNvr9UafF6TJ/zVtDgaXbFTwigLmxXvhXqOKCVD/MnRmVE7uZwh9bX6mRPqzUuoZwjAHGlJKGaIki8ndfume5vNBqvVCpvNBqPRiO3bt2Po0KHYvHmzP+qTJIMhHkmt01F1wr25vKtO5iGpdToMhjj/FOYBf9zgaR9dWTAsvEkI2EdZ5DpjGJsV3wj1HFFShviL/czowmZyZOGwcEnO9hMo9ptzG88uFio36oZ6hgDMkZaEcoYoMRfcbljq0+v1GDZsGLZs2YJ58+b5qibJU6lUyB42HuaTebAaS13ap8ZYAvPJPIwYfr/kOnlfNi3ORlfs6o+ySHGWjuawWfGPUMwRpWWIrzk7M2pX/wypnHLEH5prXkJFKGYIwBxxhhmivJMaXjUsdsXFxbh48aIvXko2MjPHIiJCh5KtyyBszoefhc2K0q25iIjQYeDAMQGq0D2+alqcja7YyW2Uxf4Fr//FJ98LtRxRWob4krMzo3ZKP0PqKSUdoLgr1DIEYI40hxnSkLPmRS754PY9LOnp6U2uFb148SJmzZrl08KkTq+PwdQpuVi+fDKKNy5G/MgchEUlNNmuxliC0q25sBQcRk7OWuj1MUGo1jXe3tPiyuiKnVzuZeGoin8wR5SZIb7gyplRO6Veh+4LTTJLYTONMUNqMUeaYoY41zgb5HI/nNsNy7p16xr8rFKpkJKSgs6dO/uqJtnIyBiKGTPWYNXqHJxf8SD03bKg65YFtTYKNosRVSfzYD6Zh4gIHXJy1qJXryHBLrlF3jQtjmYGa44cZgxjs+I/zJFaSswQbzma1ac5SpjtJ1CUNtMYM+QXzJGGmCHukctkHirhgwv3hBCw2WzQaJx3shUVFYiNjcUrr3wNnS7a27eVDLO5AgcObMT2nW+i6FJB3eNJrdMxYvj9yMwcK7vft8dh92aYEEIga/6fUVR6Gu+O1cKVkxRCAOM+sCApviPyFi6S1JkNpTcrVVWV+MMf+qC8vBwxMdI40+ZKjjBDlE0IgReeGwNRlo/37nI9R+553wJVXAYem7NRUjkidfact3P34KTCbEbs5MmSyREeizBHmCG+5W1GuMLVHHF7hOX3v/89Xn75ZURGRtY9tmvXLjz88MP4/vvvPatW5vT6GIwYMRHZ2RNgMpXBYjFBqzXAYIiT7R++uyMt1TU1OFtyGWdLrei/xuTWe1XjMqprahAZHu5puT6l9GZFCpgjDSkxQzxRU1ONstLzuFzmfo4k4gJqaqoRHh7Z8sYEoPkzq1I6q9ocZkhTzBFmiK9JafTF7REWjUaD0tLSBl3QpUuX0KFDB1y5csXpvko9q6Fk7oy0nLl8GUUVFW6/R3JMDFJbtXJ7P38IlWYl2CMsnuYIM0T5SkrOw2gscXu/6OhWiI9v64eKQk/9s6rOsj+YIyw8FqHmMEMCw1ejLz4fYfnHP/4BoHa47a233oJer6/7efv27RgwYIBHhZJ/CCFgMpXCYjFDq9XDYIj36AyLOyMt7Vu1QnuJNB7uCpVGJdiYI/LiqxxxR0JCOyQktPPre5BzUr7fhRkiL8wQ5XI2+uIOU7XFpe1cbljeeOMNALU3KK1fvx5hYbW7qtVqdOnSBW+//bYHZZKvmc0V2L//A+zYtb7JNazZw8YjM3Os27ODeDt7mNSxWQkc5og8+CNHSH6kdDmIHTNEHpghocWb46eqqkoAc1rczu1LwtRqNcrKyjwa/uUwrH/l5+/GqtU5qK6uqp0lpPugX2YJObGvbpaQqVNyPZoJw90b8eUgVJuVYF8S5mmOMEP8z985QvJn/7fAVG3Br1+fE5Qc4bGIdDFDyB2uHo+4fdP91KlTG9zkRtKQn78by5dPhja9L1JGzoQmKr7B84Yeg2E1lqJk6zIsXz4ZM2ascTsolDbSEqrNihQwR6QpEDlC8mfPTFfPjPoDM0SamCHkL26vdL9ixQqHIVFUVOSTgsh9ZnMFVq3OgTa9LxLHPN0kIOw0UfFIHPM0tOl9sWp1Dsxm92+Qr79KqlzVX9mVzUpwMEekJ5A5QuQtZoj0MEPIn9xuWI4fP45Ro0aha9eu6NSpEzp16oT09HSkpqb6oz5ywf79H6C6ugoJI2dCpXY+/7xKrUH8yNqh2gMHNnr0fnJuWuo3KmxWgoc5Ij2BzhEibzBDpIcZQv7kdsMyceJEdO/eHcOHD0efPn3w17/+FVqtFkuXLvVHfdQCIQR27FoPXbesZs9mNBYWlQB9tyxs3/kmPF03VI5NC0dVpIM5Ii3ByhEiTzFDpIUZQv7mdsNy9OhRzJkzB4888ghOnz6NW2+9FWvXrsW6dev8UB61xGQqRdGlAui7D3JrP123LBRdKoDJVObxe8upaWGzIi3MEWkJZo4QeYIZIi3MEPI3txuW7t27Y+3atbj22mtx+vRpXLp0CcnJySgoKGh5Z/I5i8UMAFBro9zaz769xeLeSrCNyaFpYbMiPcwRaQl2jhC5ixkiLcwQ8je3ZwnLzc3F3XffjYkTJ2LKlCno1asX1Go1brvtNn/URy3QamsXzbJZjG7tZ99eqzV4XYNUZw9joyJdzBFpkUKOELmDGSItzBDyN7cblkGDBuHcuXMAgP/3//4fRo4ciYqKCowcOdLnxQVKMFZi9RWDIR5JrdNhPrEPhh6DXd6v6mQeklqnw2CI80kdUmta2KxIm9JyRM4ZAkgnR4hcpbQMAeSdI8wQ8je3GxagdoXZixcvIikpCT169EBSUpJsvlT1KWElVpVKhexh47Hh/SWwGktdutmtxlgC88k8jLr7SZ/+f5NK08JmRR6UkCNKyBBAWjlC5ColZAigjBxhhpC/uX0PyxdffIGuXbsiNTUV586dw8KFC9GpUyccPXrUH/X5TX7+bsx9Ygg2vL8E5vhUJI6ei+Rxi5E4ei7M8anY8P4SzH1iCPLzdwe71BZlZo5FRIQOJVuXQdisTrcVNitKt+YiIkKHgQPH+LyWYN/TwmZFHpSQI0rKEEBaOULUEiVkCKCsHGGGkD+53bBMmzYNM2bMQHR0NABg2bJlmDRpEqZMmeLz4vzFvhKrJqUnUqatQ+LoOTD0GAxdWh8YegxG4ug5SJm2DpqUnli+fHKLQSGEgNFYguLiszAaSwI+PZ9eH4OpU3JhKTiM4o2LUWMscbhdjbEExRsXw1JwGI9MXe63MzbBalrYrMiH3HNEaRkCSC9HiJyRe4YAyssRZgj5k0q4+RcdFxeHo0ePonfv3vjmm2/QoUMHnD17FhkZGSgvL3e6b0VFBWJjY/HKK19Dp4v2qnBPmc0VmPvEEGhSeiJxzNNOFzcSNiuKNy6G9dx3WLpkT5MvlX0Yd/vON1Fc+FPd44nJaRgx/P6AD+Pm5+/GqtW1CzHpu2VB1y0Lam0UbBYjqk7mwXwyDxEROjwydTl69Rri93p6HN4CAH6/PIyNivuqqirxhz/0QXl5OWJiAv+Phac5wgzxP6nlCElXMHOExyINX0tKOcIMIXe4miNu38Ny/fXXY+fOnVCpVFCpVLBYLFi6dCn69+/vVcGBYl+JNcWNlVjPr3gQBw5sxIgRE+uey8/fjZWrZqC62gx1hL7BfiXlRXhvw7P494cv45Gpy5GRMdQfv0oTGRlDsXTJHhw4sBHbd76Jok3P1T2X1Dodo+5+EpmZYwMW0PXvaXGXq00OmxV5knOOKDlDAOnlCJEjcs4QQNk5wgwhf3C7YVm5ciXuuOMOlJeXY/jw4SgsLERKSgo2b97sj/p8ytuVWLOzJ0ClUiE/fzdycx+CUKsBlRra9H7Qdx9UdwbBfGIfzCfzUF1zBbm5DyEn57WABYVeH4MRIyYiO3sCTKYyWCwmaLUGGAxxQbmpzZNGwt0mh82K/Mg1R0IhQwDp5QhRY3LNECA0coQZQr7mdsPSuXNnHDlyBAcPHsTZs2eRmpqKAQMGICzMownHAsq+EmviwHFu7afrloWiTc/BZCqDWq3BihXTICCg7dgHibfOahI4hh6DYTWWoviTV2Ep+BIrVkzD88/vD+iQrEqlQlRUPKJcDEMpYQOifHLNkVDKEEDeOULKJtcMAUIrR5gh5Csu3XT/5ZdfoqioqO5njUaDrKws3HPPPcjKypJFQAC+WYl11671uFpTDW36/yF57Lxmz45oouKRPHYetOn/h6s11di9+y3viieSOSXkCDOEKHiUkCEAc4TIEy41LLfddhtKSmpne+jVqxdMJpNfi/IXb1dijYzUY9t/XgcAJN46y6XrTlvdOhMA8NnnrwVl5h8KvGDP1CJVSsgRZggFCnOkKSVkCMAcocBRUo64dDqisrIS8fG13fvx48dhs9n8WpS/eLsSqxA2mIxl0Hcf5PZ1p6aTeTAaSxEdneBp+SRxSlj8y5+UkCPMEPI35kjzlJAhAHOE/E+JOeJSw5KdnY1Ro0bhmmuuAVA7/3lEREST7V5//XXfVucjQgiYTKWwWMzIGngnNm1+1aOVWEtLLwLCBn33QW69v777IJhP7EVp6QWGhEI1nsYxceC4Bjc+bnh/CT7c9AqmTskN6M3TUqKUHBnQfxQ+/uRvzBDyOeaIc0rJEB6LkD8pNUdcaljeeecdrFu3DpcvX4ZKpUL79u2h1Wr9XZvXmuswVWoNLn/yKpLGzmtx7vP6K7EWF/8MwPPrTkmZ7It/adP7ImXkzGZvfCzZugzLl0/GjBlrZBUSvqKoHFGpUfzJK0ge+2dmCPkEc6RlisoQ8FiEfE/JOeJSwxIVFYUZM2YAABYuXIgnnngiKIvNucNZh1n55RZU/fglCj9YhFa3zkJYVNMzDTXGEpRurV2xNSdnLfT6GMTHtwXg+XWn9v1JOczmCqxanQNtel+ni39pouKROOZpFG9cjFWrcxwu/qV0SsoRy89HUHFgAzOEfII54holZQiPRcjXlJ4jbk+p8fbbb8NgMPijFp9xpcM0fbsLxR+/jHN/mwh9t6wGc5fXX4k1J2dt3UqsUVEJiIpOhPn4XreuOzWf2IuomERO66dAvlr8K9TIPUd0aX2gTc1A0YdL/5chmdB3H8wMIY8wR9wn9wwBeCxCvqX0HHG7YRk3zr15wwPN1Q7T0GsYtGl9cOmtJ1B16gDMJ/bWPdfcSqwqlQo33fgg/v3hS25ed7ofd/7mUS6WpDC+WvwrFCkhR3Sd/g+p096A8ejnKNv7Fswn9tU9xwwhVzFHPKOEDAF4LEK+EQo54tK0xnJi7zATXOgwNfpYJN+7GELY8JvfPIpnn92Fl176As8s3IYRIyY2CAi7YcPuQ3h4JIo/eQXCZnX6+sJmxeWPX0V4eCSGDr3Pq9+LpMe++Je7Nz7qumWh6FIBTKYy/xRGXnM1R9TaKMRc/xu0fXgFoFJj1KgcZgi5hTmiTDwWoUAKhRxRVMPiaYdp6D4I+/ZvRKtWKYiKinfaZer1MZj2yN9w5aevUfjBItQYSxxuV2MsQeEHi3Dl9Nf4/bQVsrg+kNzji8W/SHo8yZHw6FYwdB+Eg4e2wGCIY4aQy5gjysNjEQq0UMgReSwL6yJ7h5k40L2hYl23LBRteg4mU5lL13ZmZAxFzoy1WLV6Bs6teBD6rgMbXL9uPrEX5lMHEBmhxcyc1+quOyVl8XbxL61W2tdfh6pA5AgzhOyYI8rDYxEKtFDIEUU1LL7oMF29GS0jYyiWLtmLAwc2YvvON1G06bm655Jap+N2B9edkrJ4u/iXwRDnv+LIY4HKEWYIAcwRJeKxCAVaKOSIohqWQHeYen0MRoyYiOzsCTCZymCxmKDVGlq8JERJ6i+EpdXqYTA4H8ZWEpVKhexh47Hh/SUeLf4VKp+T3AQyR5ghtZgjzBEl4bFI4IVyhgChkSOKaliC1WGqVCpERcWH1FSBzS2EldQ6HdnDxiMzc2xIXCubmTkWH256BSVblzmdCQZouvgXSVMwciQUMwRgjtgxR5SFxyKBwwz5hdJzRFE33ds7TPPJPFiNpS7tY+8wRwy/XxYdphTk5+/G3CeGYMP7S2COT0Xi6LlIHrcYiaPnwhyfig3vL8HcJ4YgP393sEv1O70+BlOn1C7qVbxxsdMbH4s3Loal4DAembo8ZAJUjpgjgcEc+QVzRFmYIYHBDGlI6TmiEkKIQL1ZRUUFYmNj8corX/vtekqzuQJznxgCTUpPlzrM4o2LYT33nWxW+gy2+gthJThYCAsArMZSlGxdBkvBYcyYsQYZGUODUGlgNV7NWNcty+HiX49MXS6JGx+rqirxhz/0QXl5ueRXiq4vEBkCMEf8jTniGHMkMHgsIn/MkOYpNUcU17AADf+Q40fmICwqock2NcYSlG6t7UTrryBLzWMAO2c2V/xy42OjoekRw++X1I2PPNBoGXPEP5gjzjFH/I/HIvLGDGmZEnNEUfew2GVkDMWMGWuwanUOzq940GmHyYBwnX0hrBQXFsJSqTWIH1n7+R84sBEjRkwMTJFBxBsflYU54h/MEeeYI8rBDPEPZkjLlJgjimxYAPtUf3uanepvFKf6c4unC2Hpu2Vh+843kZ09QbZfEneF4o2PSsUc8S3miOuYI8rADPEtZoh7lJQjim1YAGV2mMESqIWwiKSGOeI7zBEKRcwQ32GGhC5FNyx2SuowgyWQC2ERSRFzxHvMEQplzBDvMUNCl6KmNSb/CfRCWESkPMwRIvIGMyR0sWEhl9gXwqo6sc+t/bxdCIuIlIM5QkTeYIaELjYs5BIuhEVE3mKOEJE3mCGhiw0LuSwzcywiInQo2boMwmZ1uq2wWVG6NRcREToMHDgmQBUSkdQxR4jIG8yQ0MSGhVym18dg6pTaBa6KNy5GjbHE4XY1xhIUb1wMS8FhPDJ1ecgs1ERELWOOEJE3mCGhKSRmCSPf4UJYROQt5ggReYMZEnrYsJDbuBAWEXmLOUJE3mCGhBY2LOQRLoRFRN5ijhCRN5ghoYMNC3mFC2ERkbeYI0TkDWaI8vGmeyIiIiIikiw2LEREREREJFlsWIiIiIiISLLYsBARERERkWSxYSEiIiIiIsliw0JERERERJLFhoWIiIiIiCSLDQsREREREUkWGxYiIiIiIpIsya10r1YLhIfbgl2Gz1VXqyGEKthlEBERERHJioQaFoE2bSxITrZCrVbagb3AlSvA99/rcfUqB7WIiIiIiFwlmYalTRsL2rWzoVWrZERE6KBSKadpEcKGy5cvol07C06f1gFQzu9GRERERORPkmhY1GqB5GQrWrVKRlRUfLDL8Yu4uESYzecRFiZQU8OGhYiIiIjIFZJoWMLDbVCrVYiI0Hn9WkIIlJRchslkhMEQhYSEVpIYrdFowqFWq6DRCNTUBLsaIiIiIiJ5kNQNFd40FuXlZVi9ejkGZvZGz56p6N+/B3r2TMXAzN5YvXo5ysvLfFeoB6TQNBERERERyY2kGhZPbd++DX36dsW8+XNQGNkWiaPnInncYiSOnovCyLaYN38O+vTtiu3btwW7VCIiIiIicoPsG5bt27dh/Pg7Idr0QMoj65B4x+Mw9BgMXVofGHoMRuIdjyPlkXUQbXpg/Pg7fd60HDlyGNnZA9ChQxzuvvs2FBUV+vT1iYiIiIhCmawblvLyMjz08H2ITO+LxDufhqaZG/Y1UfFIvPNpRKb3xUMP3+ezy8NsNhsmTboXN998Kw4cOAadTod58x7zyWsTEREREZHMG5Z3330T5iozEm6ZCZVa43RblVqD+FtyYK4y47331vvk/fPydqO0tBSPPTYP7dqlYvbsp/DJJ5tgMpl88vpERERERKFOtg2LEAKvvb4K+m6Dmh1ZaSwsKgH6bllY+9pKCCG8ruG//92Pvn37IyysdrK1a665DlarFUePHvb6tYmIiIiISMYNS0nJZRT8eAq6bllu7afrmoWCH0+htLTE6xoKCy+hVatWdT+r1WrExsbzPhaJE0LAaCxBcfFZGI0lPmleiSh0MEOIyFvMEfdIYh0WT5hMRgCAWhvl1n727Y3GSiQktGph65Y1/vuq/YOT/hTGQgiYTKWwWMzQavUwGOIVP/Wy2VyB/fs/wI5d61F0qaDu8aTW6cgeNh6ZmWOh18cEsUIieQm1HGGGEPlWqGUIwBzxlGwbFoOhtvGwWYxu7WffPioq2usaWrdug1Onjtf9bLVaUVFRhuTk1l6/tr+E6hclP383Vq3OQXV1FfTdspA4cBzU2ijYLEaYT+zDhveX4MNNr2DqlFxkZAwNdrlEkhaKOcIMIfKdUMwQgDniDdk2LAkJrZDeqSsKT+bB0GOwy/tVncpDeqeuiI9P8LqGgQMHITf3JdTU1CAsLAxHj36NsLAwXHttH69f21uOzlp8++2ekPyi5OfvxvLlk6FN74uUkTOb3PNk6DEYVmMpSrYuw/LlkzFjxhpF/f5EnmKO1GKGEHmGGfIL5oh3ZNuwqFQqPDRpKubNnwOrsdSlG+9rjCUwn8zDw4ue98mQ4w03DEKrVol4/vlFmDhxCl566f/h178eDb1e7/Vre6q5sxax8W1RUXYJ2o7XIeW2P4XMF8VsrsCq1TnQpvdF4pinm51NThMVj8QxT6N442KsWp2DpUv2KPLsDpErmCO/YIYQuY8Z0hBzxHuyvekeAMaNux96nR4lny6DsFmdbitsVpR9mgu9To977hnvk/dXq9V47bW38Nlnn+CGGzJgsViwaNHzPnltT+Tn78bcJ4Zgw/tLYI5PReLouUgetxiJo+fianJnCACW8ydQXfijw/3tXxRtel+sWp0Ds7kisL+AH+zf/wGqq6uQMNLFqa9H1p71OXBgY4AqJJIW5khDzBAi9zBDmmKOeE/WDUtsbBxeW/sWrhQcRvG/FqPG6HjmrxpjCYr/tRiWgsN4/bW3EBsb57Maevfui507D+HMmXJs2PAREhOTfPba7rAPNWpSeiJl2jokjp4DQ4/B0KX1gaHHYCT9Zi5Sf/93aNtnoPCDRaj68UuHr6OkL4oQAjt2rYeuW5bbU19v3/kmZ+ygkMMcaYgZQuQeZkhTzBHfkHXDAgAjRvwK69f/C6qLx3F+5YMo3vQcTN/tQVXBYZi+24PiTc/h/MoHobp4HG+99S9kZ/8q2CX7XOOhxua+EJqoeCSNeRq69H4o+vC5ZicsUMoXxWQqRdGlAui7D3JrP123LBRdKoDJVOafwogkiDnSFDOEyHXMEMeYI74h+4YFqG1avj58CosXPY/kKxdRvOk5FL43D8WbnkPylYtYvOh5fPP194psVgD3hxoTRuZA1FyB8dj2ZrdTwhfFYjED8Hzqa4vF5POaiKSKOdIUM4TIdcwQx5gjvqGIhgWovTxs8uTpOLD/CI4fP4cvvjiO48fP4cD+I5g8eTpiYmKDXaJfeDPUWHn442bPWijhi6LV1k5+4OnU11qtwec1EUkRc8QxZgiRa5ghzWOO+IZiGhY7lUqFhIRW6NAhDQkJrRS/AJGnQ436blmoKTkLm6XS4fNK+KIYDPFIap2OqhP73Nqv6mQeklqnw2CI809hRBLDHHGMGULkGmZI85gjvqG4hiXUeDvUKKqrHD6vhC+KSqVC9rDxMJ/Mg9VY6tI+9qmvRwy/X/HNLpEdc8QxZgiRa5ghzWOO+AYbFpnzdqhRFaFr8pySviiZmWMREaFDyVbXpr4u3ZqLiAgdBg4cE6AKiYKPOdI8ZghRy5ghzjFHvKfIhmXXrv9gxLA+2LXrP8Euxe88HWo0n8xDWEIq1NroBo8r7Yui18dg6pRcWAoOo3hjC1Nfb6yd+vqRqcu5UBOFFOZI85ghRC1jhjjHHPGebFe6b44QAs8t+TOOfXcczy35M4YOHSH7ztwZ+1DjhveXwGosdelmN/tZi/jshxp8NjXGEpRurf1C5eSsVcwXJSNjKGbMWINVq3NwfsWD0HfLgq5bFtTaKNgsRlSdzIP5ZB4iInTIyVmLXr2GBLtkooBijjjHDCFyjhnSMuaIdxTXsOzY8Tm++OpL/HFgBF4+8CV27PgcI0b4dzrjS5cuYMqUB7B48Uu49trrnGwpYDaXo7TUDK1WD4Mh3ifNVGbmWHy46RWUbF2GxDFPO51OUNisuPzxq4BKBVWEDlUFh0Pii5KRMRRLl+zBgQMbsX3nmyja9Fzdc0mt0zHq7ieRmTkWOl20k1chCj4hBEymUlgszJFAYoaQUjBDgoc54jmVCOBqPBUVFYiNjcUrr3zd4H9GZKQVPXtakJraEeHhkR6/vhACt906GGFFR5H3YCSy3riCmqRr8dEne/02yvLoo9Pxz3++BgD4z38OOmxYbDYrysuL8NNPpzBlysM4ffo0gNo/zuxh45GZOdbrMwj21WW16X0RPzIHYVEJTbaxn7Wo+vFLxMQmobzsUt1zSa3TMWL4/SHxRakN6zJYLCZotQYYDHGKHoVzpKqqEn/4Qx+Ul5cjJkY+Z6+ay5BQYDZXYP/+D7Bj13oUXSqoe5w5EnjMkFrMEXlhhkgLc6SWqzmiqIZl+/ZtuPfe27F1vB63dAnDp9/XYOR6M955Z7PfRlkuXy6GyWRE//49HDYsVVWVKCr6GULYcLm8EnPf2o/zJlF7JuHEvrozCVOn5CIjY6hXteTn78aq1Tmorq5yOtT4yNTl6NlzML8oIYwHGvLS5LvdfdAv323mCAUJc0Q+mCEkVSHXsDQeXVGpVBBCBGSUBQCSk7VNGpaqqkoUFp6GOlIPjSEOhRcvYMGOQpyr/GWGCKuxFCVbl8FScBgzZqzxOijM5opfhhobnUEJlbMW1DIeaMhH/TOWCSNnOrw2nDlCwcAckQdmCEmZqzmimHtY7PeubB2vr2tMVCoVFgwNw8j1gbmXpT6bzYqiop+hjtQjLL4tRE21w+00UfFIHPM0ijcuxqrVOVi6ZI9XQ7J6fQxGjJiI7OwJPGtBJHNmcwVWrc6BNr2v02vCmSNE5AgzhJRCEdMaCyHw4vMLMLB9BG7u3PDLeHNnDQa2j8CLzy9AAAeTYDSWQggbwmKTW9xWpdYgfmTt8OmBAxt98v4qlQpRUfFITExFVJRvbqgjosDav/8DVFdXIWHkTKc3sALMESJqihlCSqGIhsU+urJgaFiTL4N9lOWLr2pHWQJDoNJYUruCawsBYRcWlQB9tyxs3/lmQBsrIpImIQR27FoPXbcsl6YIBZgjRPQLZggpiewbFmejK3aBHmWxWq2ouXqltmFxg65bFoouFcBkKvNPYUQkGyZTKYouFUDffZBb+zFHiAhghpCyyL5hcTa6YhfoURYhbP97Y9dGV+zsDY7FYvJ1SUQkMxaLGQDcPvHBHCEigBlCyiLrhsWV0RW7QI6yqFT/+1iF1fmGjdgsRgCAVmvwdUlEJDNarR7AL7ngKuYIEQHMEFIWWTcsroyu2Pl7lKWw0FI3pbFGo0FYeKTbIVF1Mg9JrdNhMMT5vD4ikheDIR5JrdNRdWKfW/sxR4gIYIaQssi2YbGPrnRO0CBRr8JXF6wt/peoV6FzgiYAoywqREcl1DYsNtdGWWqMJTCfzMOI4fdzFg0igkqlQvaw8TCfzIPVWOrSPswRIrJjhpCSyHYdlurqapw/fwbnS6zov8a96yxTIs6iuroakZGeLVLpiqioeJSVXUJNeSHC4ts63VbYrCjdmouICB0GDhzjt5qISF4yM8fiw02voGTrMqdrKADMESJqihlCSiHbhiUyMhIffbIPly8Xu71vYmKSX5sVAFCrNUhK6oDCwtOoKb0AdTNDqzXGEpRuzYWl4DByctZ6tVATESmLXh+DqVNysXz5ZBRvXIz4kTkIi0posh1zhIgcYYaQUsi2YQGAlJT2SElpH+wymqXTRSM5uSOKin5GzRUTbFWVMP/wJapMAjaLEVUn82A+mYeICB1yctaiV68hwS6ZiCQmI2MoZsxYg1Wrc3B+xYPQd8uCrlsW1Noo5ggRtYgZQkogqYZFiYsU6XTRSE3tgfLyQpSUlKLkP6tR+PPPAICk1ukYdfeTyMwcC50uOsiVEpFUZWQMxdIle3DgwEZs3/kmijY9V/ccc4SIWsIMIbmTRMNy9aoaNptAdXUVIiK0wS7H59RqDbTaKMTEtMbs2RtRWWmCVmuAwRDHm9qIyCV6fQxGjJiI7OwJMJnKYLEwR4jIdcwQkjNJNCw2mwqFhRqEhRUBACIidIr68ghhQ1lZMcrL1YiMjENkZHywSyIimVKpVIiKikdUFHOEiNzHDCE5kkTDAgAXL2oBWFBTUwi1WjnNSi2BK1eA8+f1AJT2uxERERER+Y9kGhZAhYsXdSgsFAgPtwW7GJ+rrlZDCDYrRERERETukFDDUstmU+HKlebnCSciIiIiotAh25XuiYiIiIhI+diwEBERERGRZAX0kjD7OisWizGQb0tEjdi/g3Jb+4gZQiQdzBEi8parOaISAUyas2fPon176a5MTxRqzpw5g9TU1GCX4TJmCJH0MEeIyFst5UhAGxabzYbz588jOjpaUeusEMmNEAKVlZVo164d1Gr5XBnKDCGSDuYIEXnL1RwJaMNCRERERETkDvmcEiEiIiIiopDDhoWIiIiIiCSLDQsREREREUkWGxaZq6ysxKhRo6DX65GcnIxDhw4FuySf+eqrr9CnTx/odDrcfPPNKCwsDHZJRIrEHCEibzBDyN/YsMjcunXrcPHiRfzwww/YtWsXOnTo4PVrDh8+HOvWrfO+OC/YbDaMHTsWo0aNwqlTp6DT6fDHP/4xqDURKRVzhIi8wQwhfwvowpHke5cvX8Y111yDtm3bom3btsEux2d27dqFkpISLFiwAGFhYZg/fz4GDx4Mk8kEg8EQ7PKIFIU5QkTeYIaQ3wmSpbffflsAaPBf9+7d654/deqUuPHGG0V0dLTo16+f+O9//1v33N69e0WfPn2ETqcT119/vcjPzxdCCDF16tQmrzl16lQhhBA7duwQHTt2bFADAFFQUCCEEGL+/PliwoQJ4pNPPhHXXXedmDt3rku1NOeZZ54RN910U93PVqtVREREiN27d7v9WRGRY8wRIvIGM4QChZeEydTYsWNRWlqKOXPm4Le//S1KS0vrrhmtqanBHXfcgQceeADHjh3DAw88gN/+9rcQQsBms+Guu+7CmDFj8OOPP2Lw4MGYPXs2AODll19GaWkpBg0ahL/+9a8oLS3Fyy+/7HJNx44dw5w5czBv3jxMnjy5xVqcuXjxIhITE+t+VqvViI+Px6VLl9z9qIioGcwRIvIGM4QChZeEyVR4eDji4uKg1WoRERGBuLi4uucOHjyI48ePY9asWXWPlZeX48KFC2jTpg0OHTqEpKQkHDt2DBUVFThx4gQAQKfTQafTISwsDHq9vsFruuLYsWM4fvw40tLSXKqlXbt2Tl+vcZAIIbgqMZEPMUeIyBvMEAoUNiwKdPbsWXTs2BE7duxo8HhSUhLUajX+9re/YdWqVejQoQM6duwIq9Xq9nuYTKYmj91+++0NAqKlWpxp27Ytvvvuu7qfrVYrysrK0KZNG7drJSL3MUeIyBvMEPIlXhKmQKmpqSgsLERycjLS0tKQmpqK559/HpcvX8auXbuwcuVKHD16FIcPH8aUKVOa7K9Wq5ucUVCpVA3C5Isvvmiyn6Mb0JzV4syQIUNw6NAh1NTUAAAOHz6MsLAw9O3b16XPgIi8wxwhIm8wQ8iX2LAo0IABA5CSkoLZs2fjzJkzWLJkCT766CMkJSWhoqICKpUKRqMR+/btw5/+9KcmgdClSxds374dFy9exI4dO2C1WpGSkoKLFy+ioKAAJpMJ8+fP97oWZwYPHoykpCTMnz8fZ8+exaJFizBmzBjo9XqPPxcich1zhIi8wQwhX2LDokDh4eHYvHkzjh8/jl69emHLli348MMPodFoMHLkSPzqV79Cv3798Mgjj2Dy5Mk4f/58gxvI5s2bh4KCAnTo0AEPPfQQbDYbunTpglmzZmHQoEENbo7zphZn1Go1NmzYgC1btqBLly6wWCz4y1/+4tXnQkSuY44QkTeYIeRLKtHSFAlERERERERBwhEWIiIiIiKSLDYsREREREQkWWxYiIiIiIhIstiwEBERERGRZLFhISIiIiIiyWLDQkREREREksWGhYiIiIiIJIsNCxERERERSRYbFiIiIiIikiw2LEREREREJFlsWIiIiIiISLL+P39TQ0SGA3cQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建一个图形和三个子图，子图排列为1行3列，每个子图的尺寸为10x3\n",
    "fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n",
    "\n",
    "# 遍历邻居数列表（1, 3, 9），并为每个邻居数分配一个子图\n",
    "for n_neighbors, ax in zip([1, 3, 9], axes):\n",
    "    # 创建一个K近邻分类器，设置邻居数为当前的邻居数，\n",
    "    # 并使用数据X和标签y来训练模型。\n",
    "    clf = KNeighborsClassifier(n_neighbors=n_neighbors).fit(X, y)\n",
    "    \n",
    "    # 在子图上绘制模型的决策边界\n",
    "    mglearn.plots.plot_2d_separator(clf, X, fill=True, eps=0.5, ax=ax, alpha=.4)\n",
    "    \n",
    "    # 在子图上绘制数据点，颜色根据标签y来确定\n",
    "    mglearn.discrete_scatter(X[:, 0], X[:, 1], y, ax=ax)\n",
    "    \n",
    "    # 设置子图标题\n",
    "    ax.set_title(\"{} neighbor(s)\".format(n_neighbors))\n",
    "    \n",
    "    # 设置 x，y 轴标签\n",
    "    ax.set_xlabel(\"feature 0\")\n",
    "    ax.set_ylabel(\"feature 1\")\n",
    "\n",
    "# 在第一个子图的左下角（loc=3）添加图例。\n",
    "axes[0].legend(loc=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e27058b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 加载数据集\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "# 将数据集分为训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    cancer.data, cancer.target, stratify=cancer.target, random_state=66)\n",
    "\n",
    "# 初始化空列表，用于存储不同邻居数量下的训练集和测试集准确率。\n",
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "\n",
    "# _neighbors 值从 1 到 10\n",
    "neighbors_settings = range(1, 11)\n",
    "\n",
    "for n_neighbors in neighbors_settings:\n",
    "    clf = KNeighborsClassifier(n_neighbors=n_neighbors)\n",
    "    # 训练数据\n",
    "    clf.fit(X_train, y_train)\n",
    " \n",
    "    # 记录训练精度\n",
    "    training_accuracy.append(clf.score(X_train, y_train))\n",
    "    # 记录泛化精度\n",
    "    test_accuracy.append(clf.score(X_test, y_test))\n",
    "    \n",
    "# 绘制折线图\n",
    "plt.plot(neighbors_settings, training_accuracy, label = \"training accuracy\")\n",
    "plt.plot(neighbors_settings, test_accuracy, label = \"test accuracy\")\n",
    "\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.xlabel(\"n_neighbors\")\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d64b1296",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d73acf8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03d5f182",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ec4efa0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81de6fe3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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